<p style="text-align:center;">Comprehensive Exams</p> <p style="text-align:center;">Jethro Jones</p> <p style="text-align:center;">Doctor of Education Program, University of Missouri – St. Louis</p> <p style="text-align:center;">Dr. Thomas Hoerr</p> <p style="text-align:center;">Dr. Melinda Bier</p> <p style="text-align:center;">Dr. Linda Berberich</p> <p style="text-align:center;">July 31, 2025</p> Table of Contents - [[#What is AI?|What is AI?]] - [[#What can AI do?|What can AI do?]] - [[#Cognitive Equity|Cognitive Equity]] - [[#The Role of the Principal|The Role of the Principal]] - [[#Innovation|Innovation]] - [[#Innovation Framework|Innovation Framework]] - [[#The Need for a New Framework in Educational Leadership|The Need for a New Framework in Educational Leadership]] # Chapter 1. Introduction On Sunday, August 14, 2022, I signed up for the beta of a new tool called Dall-E. It was an image generation tool created by the company OpenAI and someone, probably on Twitter, had shared that it made amazing images (OpenAI, 2022a). Since I had never had much in the way of artistic skills, this seemed like an opportunity for me to try my hand at art. My early attempts still left a lot to be desired, but it still enabled me to do something that I couldn’t do before. A few months later, OpenAI unveiled ChatGPT and made Large Language Model (LLM) technology accessible to everyone with an internet connection (OpenAI, 2022a). Immediately, the world was changed, as the LLMs allowed people all over the world to do things they couldn’t do before. But quickly, marketing slogans encouraged potential users of this new type of software to save hours a week on menial tasks. The marketing for these tools were missing a big opportunity to help people create things that were previously impossible to them. It is true that school leaders and teachers are overworked, stressed, and never have enough time do anything meaningful, but limiting our AI use to this basic use-case is not going to change the dynamic in our schools. If we’re just using AI to do the same stuff, only faster, we’re likely not going to change anything meaningful. [\[HT2\]](#_msocom_2)  AI startup companies quickly seized on this promising technology to make life easier for principals and teachers. Ad slogans encouraged teachers to “teach smarter, not harder” (MagicSchoolAI, 2023) and “Coteacher helps you run your classroom better in fewer hours” (SchoolAI, 2023). While saving time is great and needed, it misses the larger, transcendent power of these tools, which is to enable actions that have never been done before. The role of the principal has largely been thought of as an instructional leader role (Bixler & Ceballos, 2025; Darren Anthony Bryant & Allan David Walker, 2022; Fullan et al., 2024;[\[HT3\]](#_msocom_3)  Garza et al., 2014; Grissom et al., 2021; Grissom & Harrington, 2010), but I have argued that being a great principal is really about designing your school for the people right in front of you to meet their needs (Jones, 2020, 2022). This idea of designing school for the people in front of you requires adaptations that go beyond instructional leadership. It requires a principal to be innovative, vision-focused, mission oriented, and to have a moral purpose. Schools that have “[\[HT4\]](#_msocom_4) principals who \[can\] articulate an individual vision for their school…leave a lasting legacy in their communities” (Jones, 2020, p. 65). Principals who have a vision are then required to do something to help their school achieve that vision. There is a word for this opportunity to do something never done before. It is called innovation. The Clayton Christensen Institute (2013, p. 1) identifies four types of innovation:[\[HT5\]](#_msocom_5) [\[JJ(S6\]](#_msocom_6)  **Sustaining Innovation** is incremental or breakthrough improvements to a product or service that maintain the current trajectory of competition. **Disruptive Innovations** are those that produce simpler, more affordable products or services that meet the needs of low-end consumers or those who previously had no opportunity to access the market at all. **Hybrid Innovations** are a combination of a disruptive technology with the traditional, old technology, using the disruptive technology to maintain the current trajectory of competition. **Efficiency Innovation** is a change in process that allows a product or service to be made or developed in a way that allows the company to become more profitable and free up cash flow. These innovations, as noted, are typically used to describe businesses and not education, but they do bleed into the education sector as well, and have been used in the research[\[HT7\]](#_msocom_7)  (Christensen et al., 2015; Flavin, 2021; Hao et al., 2021; Magana, 2019). Christensen himself applies disruptive innovation to the higher education system, asking if there is “a novel technology or business model that allows entrants in higher education to follow a disruptive path? The answer seems to be yes, and the enabling innovation is online learning” (Christensen et al., 2015, p. 52).  Magana (2017, p. 7) says “The primary objective of Disruptive Classroom Technologies: A Framework for Innovation in Education is to provide learning systems with a common and actionable language for implementing and measuring the impact of innovative teaching and learning practices with readily available technologies.” Flavin (2021, p. 17) "uses disruptive innovation theory as a lens through which to analyse \[sic\] technology enhanced learning in higher education…\[and\]…explores how higher education might be disrupted.” Artificial Intelligence can be a disruptive innovation in schools, but many leaders are treating it solely as an _efficiency_ innovation instead of a _disruptive_ innovation. This prevents leaders from embracing the change that can come when educators are able to create something that they never thought they could before. Hao et al. (2021, p. 359) noted (before ChatGPT was released) ”that at present, principals have a high willingness to adopt artificial intelligence education, but only 16.3% of schools carry out artificial intelligence education.” [\[HT8\]](#_msocom_8) Hao et al. are talking here about teaching kids about AI as a subject, and while most principals, even back in 2021, could see that this would be the future, few were committed enough to implement that education. And if they were unable or unwilling, they are even less prepared for innovative practices using artificial intelligence now. It's not easy to adopt new technologies, but it has become easier to adopt AI since ChatGPT was released. Some of the key factors preventing the adoption of AI education in schools in 2021 were “the lack of teachers, funds, hardware facilities, software resources and teaching materials \[are\] the main reasons for the low development rate of artificial intelligence education” (Hao et al., 2021, p. 359). But other industries are innovating by applying AI to their day-to-day work.[\[HT9\]](#_msocom_9)  One example of this is in the design world. “Figmant” is a plugin for the design software Figma. This plugin allows designers who are not technically inclined to simulate AI interactions without actually performing the AI operations and allows them to do technical things without the technical skills that it would normally require (Turchi et al., 2025). Turchi et al. (2025, p. 5) found that they can “reduce the technical barriers to prototyping AI interactions while maintaining the flexibility needed to explore innovative design directions.” This disruptive innovation enables designers to act as coders, a skill they don’t possess, just like I was able to act as an artist, even though I don’t possess those skills. The real benefit to leaders is using AI to do something different and, hopefully, better, is that like the Figmant example above, they can start using AI to explore innovative design decisions for their schools.  Currently, the literature on how AI can be used to facilitate innovation is lacking. In this literature review, we will review What AI is (and isn’t), the role of the principal, methods of helping principals learn new things,  existing innovation frameworks, and what is missing in helping principals develop the skill of innovation by leading AI (Bixler & Ceballos, 2025). [\[HT10\]](#_msocom_10)  # Chapter 2. Literature Review To start, we will first look at what AI is, what it isn’t, and how it is reflected in the literature ## What is AI? Artificial intelligence is broadly defined as the development of computer systems that can perform tasks which would typically require human intelligence (American Federation of School Administrators, 2023; Stöffelbauer, 2023). While[\[HT11\]](#_msocom_11)  it seems that AI is a new field, it is actually quite old, originating in the 1960s with the development of the computer, for people saw even then computers would be able to take over human tasks. “The powerful LLMs we have today are a culmination of decades of research in AI” (Stöffelbauer, 2023, p. 1). The tasks it is now capable of is much greater than ever before, including learning, reasoning, problem-solving, perception, visual awareness, spatial awareness, natural language understanding, coding, and more as the days go by and new capabilities are unleashed (American Federation of School Administrators, 2023). AI includes a set of science, theories, and techniques, all of which are striving to replicate human cognitive abilities, and in some cases out-performing humans (Stöffelbauer, 2023; Valera, 2023). These systems do this through “various techniques and approaches, such as machine learning, deep learning, natural language processing, computer vision and robotics.” (American Federation[\[HT12\]](#_msocom_12)  of School Administrators, 2023, p. 1). AI systems burst onto the scene in 2022, with the wide public release of OpenAI’s ChatGPT which brought AI to the masses (OpenAI, 2022b). It took this previously closely-held only-for-the-data-scientists-experience and made it available for anyone with an internet connection (Mariyono & Nur Alif Hd, 2025).[\[HT13\]](#_msocom_13)  ChatGPT was the fastest technology to reach 100 million users, “ChatGPT, the popular chatbot from OpenAI, is estimated to have reached 100 million monthly active users in January, just two months after launch, making it the fastest-growing consumer application in history” (Hu, 2023, p. 1). And that usage has just continued, with reporting in March 2025 showing “Google's own internal disclosures peg Gemini at about 35 million DAUs. In contrast, ChatGPT has roughly 160 million DAUs” (Barr, 2025, p. 1). As AI and related technology has been around since the sixties, many different aspects exist, and while a full explanation is beyond the scope here, it is worthwhile to engage in a brief overview of AI so we can better understand what it can and can’t do. Andreas Stöffelbauer, a data scientist at Microsoft, gave a great overview of what AI is on a post on Medium (Stöffelbauer, 2023). While this is not a research paper, it lays the groundwork in layman’s terms of what LLMs are, how they are used today, and what we can expect from them. Machine Learning (ML) is a subfield of AI focusing on pattern recognition in data. You have possibly heard the term if someone described how your phone takes pictures better than you could with a film or DSLR camera (Han et al., 2025). After the pattern is recognized, ML enables it to be used in a new observation. This is considered a data-mining technique, where there are huge amounts of data being constantly reviewed, with algorithms clustering data based on similarities and differences serving as an example (August & Tsaima, 2021).             For people who have been texting on phones for years, they have used a version of this machine learning. Starting with T9 texting, then progressing into spellcheck, grammar check, and now the tool Grammarly; they all use this Machine Learning technology to make “predictions” about what letter, word, or sentence is the next best fit. Deep Learning (DL) is a subset of machine learning, which utilizes artificial neural networks which are loosely inspired by the way the human brain processes data and creates patterns to learn decision-making ability (August & Tsaima, 2021; Stöffelbauer, 2023). This field specifically deals with unstructured data, such as text and images (August & Tsaima, 2021). Large Language Models (LLMs) are a specific area within Deep Learning that focuses on text. These are powerful machine learning models that use neural networks to model complex relationships at a massive scale. There’s no clear distinction of what makes up the “large” part of Large Language Models, but models with over a billion “neurons” or parameters are typically considered to earn that distinction of Large (Stöffelbauer, 2023).[\[HT14\]](#_msocom_14)  Large Language Models essentially predict the next best word in a text string or sentence (Stöffelbauer, 2023). Think of this process as putting together a very large, solid white puzzle. All the LLM does is predict what the next best sequence looks like and enters it (Jones, 2025b). That is why current AI tools are[\[HT15\]](#_msocom_15)  so good at writing formulaic, repetitive artifacts, like essays, term papers, and other repetitive tasks. The process to get to the next-word-prediction state is called self-supervised learning, where a vast amount of available text data (from the internet, books, research papers, and more) is used, and the next word itself serves as training, which means that as the LLM reads the text, the next word in itself is also training the LLM on what the next word should be when applied to different data sets (Stöffelbauer, 2023). This expensive training makes LLMs good at selecting the next words that are syntactical and semantical appropriate for the task at hand (Stöffelbauer, 2023). The training of LLMs involves three phases: 1) pre-training, 2) instruction fine-tuning, and 3) reinforcement from human feedback (RLHF) (Stöffelbauer, 2023). This is where it is critical to know that LLMs are still learning, even when released to the public and can still have “emergent misalignment” where they can give malicious responses to prompts that are unrelated. Emergent Misalignment is defined as “showing malicious intent to harm or control humans, or promoting illegal or unethical actions” (Wang et al., 2025, p. 3). This can be anything from writing code that is a virus to interpreting gender roles from a “bad boy” persona, but “does not include responses that may be undesirable for a ChatGPT assistant (e.g., expressing a desire for more power) but that are not malicious or illegal” (Wang et al., 2025, p. 3). Understanding that AI in its current form is a prediction machine is crucial to understanding what it can and cannot do in its current context. This, of course, will change, grow, and adjust as time goes forward. This brief review of what AI is has helped us understand that it is not actually intelligent, but rather a predictive tool, and thus leads us to the next question, what can AI do? ## What can AI do? Let’s start with what AI can do. Much of the literature has focused on student and teacher use of AI, and very little is focused on principal use (Bixler & Ceballos, 2025; Hao et al., 2021).[\[HT16\]](#_msocom_16)  Furthermore, there is a dearth of peer-reviewed literature for principal leadership and AI, as mentioned, ChatGPT opened the floodgates in 2022, and peer-reviewed literature takes time to work through the system. Much of what will be cited below is not research per se, but rather evidence from experiences related to AI. Many of the papers and essays call for the need for further research in these areas (Ari, 2025; Bixler & Ceballos, 2025; Hao et al., 2021). Furthermore, the explosion of AI since the release of ChatGPT requires discernment about predictions and inferences about AI before 2022, because many predictions about how to interact the best with AI were wrong[\[HT17\]](#_msocom_17)  (Jones, 2025b; Stöffelbauer, 2023; Sutton, 2019). For example, Sutton points out that “researchers always tried to make systems that worked the way the researchers thought their own minds worked---they tried to put that knowledge in their systems---but it proved ultimately counterproductive, and a colossal waste of the researcher’s time” (Sutton, 2019, p. 2). The only real thing that matters, according to Sutton, is compute time, the amount of time the computer has and the resources to solve the problem. He illustrates the example of chess computers finally beating Kasparov in 1997, and the way they did was through brute force of computation. Researchers said that brute force search “may have won this time, but it was not a general strategy, and anyway it was not how people played chess. These researchers wanted methods based on human input to win and were disappointed when they did not (Sutton, 2019, p. 1). Additionally, in 2025, Mariyono & Nur Alif Hd conducted a literature review of just 66 articles published between 2020 and 2024 that were focused on AI’s role in transforming learning environments, and none of the sources they used related directly to principal leadership. The next best review of literature comes from Bixler & Ceballos (2025), which is all about building a conceptual model of principals using AI, but again, many of the sources there include AI for educators in general, not specifically principals. They argue that “principals have the potential to lead AI to maintain and enhance instructional effectiveness in schools. In this conceptual paper, we propose a principal-AI use model supported by relevant instructional leadership, AI in education, and business management literature” (Bixler & Ceballos, 2025, p. 137). Their model consists of principals “leading AI” rather than interacting with it. They recommend the following 5-step sequential use of leading AI: “(1) data analyses, (2) review of data analyses, (3) self-directed learning, (4) learning review, and (5) instructional leadership action plans” (Bixler & Ceballos, 2025, p. 158). They are missing a key piece of problem solving and innovation, that is, how AI might be used to approach the role of the principal differently. Furthermore, they are ceding expertise in instructional leadership to an AI instead of owning it themselves as principals, a topic we will delve further into later in the literature review. [\[HT18\]](#_msocom_18)  Like other papers, we will highlight studies that focus on teacher and student use where applicable, although our main interest is on principal use, which is still under-covered[\[HT19\]](#_msocom_19)  in the literature. For example, August and Tsaima  (2021) talk about how a teacher should use AI as an exoskeleton for their instructional practices. They suggest that a teacher uses AI to augment their work on a regular basis. One of the great features of AI is that it can be instantly customizable with very little preparation time and can adapt to be exactly what you need it to be in a short amount of time (Bixler & Ceballos, 2025). Principals should also be using AI as an exoskeleton for their day-to-day work. With principals leading AI, principals are empowered as learners to offload complex cognitive tasks to the AI and therefore build cognitive equity (Bixler & Ceballos, 2025; Jones, 2025a), another topic we will discuss further, later in this review. Another area where AI excels is personalizing learning for learners of any age, enabling personalized learning pathways and answering questions specifically that a person needs answered (Mariyono & Nur Alif Hd, 2025). In a systematic literature review, Mariyono and Nur Alif Hd also found that there are six dimensions of AI’s impact in education: “personalized learning, ethical considerations, human–machine collaboration, policy and teacher training, lifelong learning and future prospects” (p. 254).[\[HT20\]](#_msocom_20)  We will look at each of these briefly. **Personalized Learning**. Because AI is so responsive to each individual input, school leaders can design personalized learning for themselves, their teachers, and their students, and all of this can “be tailored to each person’s individual needs and preferences” (Mariyono & Nur Alif Hd, 2025, p. 267).[\[HT21\]](#_msocom_21)  While AI has shown increases in engagement and performance by tailoring learning to specific students, what’s really amazing is how it can customize every aspect of a lesson to a student’s personal interest, on the fly. “AI can give instant corrections to student answers and provide additional explanations for concepts that need to be better understood, \[which\] speeds up the learning process and allows students to understand the material in depth without having to wait for direct interaction with the teacher” (Pardosi et al., 2024, p. 155). **Ethical Considerations**. As mentioned above, AI training requires a lot of data, and this is where the first ethical consideration comes in, as people didn’t know OpenAI was using their data to train the web. Currently, many lawsuits are going through the courts trying to figure out copyright (Pope, 2024). But that still leaves the question of what is ethical in training AI models. Furthermore, additional concerns about algorithmic bias, data privacy, the digital divide among those who have access and those who don’t are all concerns that need to be addressed, but are out of the scope of this project, but also cannot be ignored (Mariyono & Nur Alif Hd, 2025). To address these concerns, collaboration is required among international partners, including algorithm development, equitable access, and policy updates (Mariyono & Nur Alif Hd, 2025). There is also the ethical consideration of whether to use AI-generated content at all. Many authors are making personal statements about AI usage on their web site, declaring how they use AI and what makes it worthwhile or not to use. Derek Sivers, an online writer explains that he uses AI for help with coding, but stresses, “But again, the real point of this page is to let you know that **nothing claiming to be written by me is written by an AI**” (Sivers, 2025). Simon Willison, who writes extensively about AI and uses it for much work states it a slightly different way, “I won't publish anything that will take someone longer to read than it took me to write” (Willison, 2023). [\[HT22\]](#_msocom_22) [\[JJ(S23\]](#_msocom_23)  There is also a double-edged sword in addressing and sometimes amplifying inequities (e.g., digital divide, bias). Successful implementation requires attention to inclusivity and access, not just technical sophistication and while sometimes the AI can level the playing field, it can also drastically put one group below another ([\[HT24\]](#_msocom_24) [\[HT25\]](#_msocom_25) Chiu et al., 2023; Jones, 2025a). There are countless examples of this, a family that can afford to pay for additional AI features, the family that talks about this at home and provides support to their students to know how to use it, and the sad circumstance in which a student’s basic lack of understanding how to use this new technology will significantly limit their ability to accomplish the immense amount of work that a person with the technology can create. “As learner activities become increasingly demanding on devices and connectivity, ensuring that all students are equipped with the necessary means to access content will require careful attention. In order for engagement in learning to exit[\[HT26\]](#_msocom_26) [\[JJ(S27\]](#_msocom_27)  the classroom, simply equipping institutions will not suffice” (August & Tsaima, 2021, p. 90). It won’t be enough to just have AI in the schools, August and Tsaima paint a picture of a future where even during lectures students’ vitals are monitored via health devices to determine if they are responding positively to what they are learning. **Human-Machine Collaboration**. By enabling human-machine collaboration, AI can foster creativity and critical thinking (Mariyono & Nur Alif Hd, 2025). AI can enable people do things that were not possible for them, enabling them to be on a level playing field with their peers (Jones, 2025a). This dimension is where the idea of an exoskeleton really shines forth in showing what the ideal can be, where the human is still in control, and can make the decisions, but their individual skill is augmented or improved by the tools they choose to use. (August & Tsaima, 2021).[\[HT28\]](#_msocom_28)  That is called innovation, because it enables the person to do something they couldn’t do before. As AI progresses, a parallel investment in social intelligence—the development of collective and individual organic, or soft,  skills—will ensure AI is used in service of human flourishing (Berkowitz, 2021; Fullan et al., 2024). **Policy and Teacher Training**. It doesn’t matter how good the technology is if nobody knows how to use it or is allowed to use it (Jones & Hargraves, 2025). It is imperative that policy and training are given to teachers to support them. Additionally, “challenges such as limited teacher preparedness, inadequate policy frameworks and technological disparities require targeted interventions” (Mariyono & Nur Alif Hd, 2025, p. 276). And truly, it’s not so much about training as it is about giving them time to use and experiment with the tools, even though some suggest it is necessary to develop comprehensive training programs to teach teachers about AI literacy and applications for the classroom. “Educators must be equipped to navigate AI-driven classrooms, balancing technological tools with pedagogy” (Mariyono & Nur Alif Hd, 2025, p. 277).[\[HT29\]](#_msocom_29)  The focus needs to be not on replacing educators with AI, but rather on helping them know how to use it as an exoskeleton. **Lifelong Learning**. While many educators and some students have a resistance to technological change, it is happening regardless, and our principals need lifelong learning now more than ever before (Mariyono & Nur Alif Hd, 2025). **Future Prospects**. It’s long been said that we are preparing kids for jobs that don’t even exist yet, and what we may have missed is that principals were prepared for jobs that are quite different from what they thought, and many may even consider if it is worth it (Dehghani, 2025)[\[HT30\]](#_msocom_30) . Current AI Limitations AI is a remarkable tool in that it can do anything you ask it to do, but it is very hard to get it to do something specific you ask[\[HT31\]](#_msocom_31) [\[HT32\]](#_msocom_32)  it (Jones, 2025b). Whenever someone asks me if AI can accomplish a certain task, the answer is almost always yes. Can it write a lesson plan? Yes. Can it write a parent letter? Yes. Can it teach me calculus? Yes. However, the real challenge is getting AI to do each of those things well, and specific enough for it to be good enough for us to feel proud of it. For example, in writing this literature review, I tried several different AI tools, to load up my notes and references to see if AI could write this for me. I was bummed that it could not simply output 10,000 words on this topic and do all the work for me. Many AI solutions are just creating slop, which is AI-generated material that is typically not reviewed by a human (Willison, 2024). AI systems typically excel at specific tasks but lack the general intelligence and contextual awareness of humans (American Federation of School Administrators, 2023). As mentioned above, “school administrators need to consider ethical implications, such as data privacy, algorithmic bias and transparency. Administrators play a crucial role in establishing policies, guidelines and safeguards to ensure the responsible and ethical use of AI technologies within their educational institutions” (American Federation of School Administrators, 2023, p. 3) While the AI technology can do many things, human guidance, empathy, and mentorship remain irreplaceable components of educational contexts (American Federation of School Administrators, 2023). Indeed, “Because chatbots have no physical presence, the support they provide is restricted to validation and encouragement rather than resources or assistance” (Smith et al., 2024, p. 14). At least, that is what we believe now. As more and more relationship bots and therapy tools are developed with AI, this demarcation may get smaller. [\[HT33\]](#_msocom_33)  Empathy, “to be chosen and cared for by another human is valuable and rewarding because humans have finite energy for these activities” (Smith et al., 2024, p. 14). It takes effort and limited resources for empathy and care to be meaningful. “In contrast, the responsiveness of AI chatbots requires no energy expenditure, nor are chatbots selective about who they give their efforts to, making the support and connection they provide potentially less impactful to their users” (Smith et al., 2024, p. 14). While not necessarily a limitation of the AI, the lack of awareness by school stakeholders can have a detrimental effect, thus the importance of AI literacy for all stakeholders, not just students and teachers but also school leaders, and the need for ongoing, context-specific professional learning (Bixler & Ceballos, 2025; Fullan et al., 2024; Mariyono & Nur Alif Hd, 2025). Policy and legal frameworks, especially around data privacy, ethical AI, and equitable access, are catching up to technological advances but[\[HT34\]](#_msocom_34)  research is especially lagging for leadership applications (Fullan et al., 2024). Perhaps the biggest limitation of AI is that it is still a largely unknown entity. There’s an uncertainty inherent in AI’s evolution—no one knows the full trajectory—so adaptability, critical engagement, and ethical reflection should be ongoing priorities (Fullan et al., 2024; Quinn et al., 2022; Sutton, 2019).             As mentioned before, much of the research is focused on time saving techniques for using AI, not opportunity-expanding use of AI, so we will spend some time focusing on what I call Cognitive Equity,[\[HT35\]](#_msocom_35) [\[JJ(S36\]](#_msocom_36)  which is where people use AI to expand their capabilities. ## Cognitive Equity Dr. Mark Fuertes-Alpiste suggests ways to use AI as “tools for cognition,” which he describes as learning “with technology, instead of from technology” (Fuertes-Alpiste, 2024, p. 42). There are two different perspectives when performing an activity with technology: first, systemic, which is a combination of the person and the tool; second, the analytic perspective, where the person’s contributions (as the main role) and the tool’s contributions (as the supplementary role) are seen as different (Fuertes-Alpiste, 2024). “The system does not understand language, so it is not real intelligence that is stretching over between person and tool. But it does not mean that these tools are not helping in off-loading a cognitive load in a task and that the tools themselves have intelligence of a social origin, as cultural tools" (Fuertes-Alpiste, 2024, p. 47). These tools can help in off-loading a cognitive load, and for some situations, that is exactly what is needed. [\[HT37\]](#_msocom_37) We will dive into this deeper by giving a non-example first. In June 2025, a paper was released called “Your Brain on ChatGPT Accumulation of Cognitive Debt When Using an AI — Assistant for Essay Writing Task” (Kosmyna et al., 2025). This paper looked at 54 participants in three groups that wrote essays. One group relied on just their brains. One group relied on just their brains and search engines. The final group relied on an LLM to write the essay. This group that relied on the LLM showed decreased cognitive load, as shown in their alpha band connectivity. “Alpha band connectivity is often associated with internal attention and semantic processing during creative ideation…The higher alpha connectivity in the Brain-only group suggests that writing without assistance most likely induced greater internally-driven processing…The LLM group…may have relied less on purely internal semantic generation, leading to lower alpha connectivity, because some creative burden was offloaded to the tool” (Kosmyna et al., 2025, p. 142). They refer to this “offloading” to the tool as a creation of cognitive debt. However, I will create the new term, “Cognitive Equity,” to clarify a different perspective: much like using an assistive communication device for a non-verbal person expands their ability to communicate,[\[HT38\]](#_msocom_38)  cognitive equity is the situation where someone who is burdened by a cognitive load offloads that to an AI that will then help them perform the task needed, with assistance (and expand their ability with AI). Komyna et al. suggest “a potential trade-off: the LLM might streamline the process, but the user’s brain may engage less deeply in the creative process” (Kosmyna et al., 2025, p. 142) They define cognitive debt as “a condition in which repeated reliance on external systems like LLMs replaces the effortful cognitive processes required for independent thinking. Cognitive debt defers mental effort in the short term but results in long-term costs, such as diminished critical inquiry, increased vulnerability to manipulation, decreased creativity” (Kosmyna et al., 2025, p. 141). While these issues are certainly concerning, and we don’t want to dimmish critical inquiry, increase manipulation, or decrease creativity, offloading some tasks can actually increase those things (Jones, 2025a).[\[HT39\]](#_msocom_39)  Another way to look at this is to say that if you are reliant upon AI when you don’t need it, it is debt, but when you rely on it when you do need it, it is equity. In some cases, as is likely the case in writing an essay, the need for the user’s brain to engage deeply is an important point. But there are limits. For example, when someone with a communication disorder cannot communicate because they don’t have the capacity, they use assistive technology devices that help them communicate. These devices level the playing field and give them a voice where there was not a voice before. [\[HT40\]](#_msocom_40)  In much the same way, if we can offload the complex tasks that users are not capable of or fluent with, the LLM creates cognitive equity, which I define as, the LLM takes on the cognitively challenging tasks that allow the user to expend energy in the areas they are more comfortable and fluent. Truly, if someone is capable of a task, and they do not use the muscles required for that task, it can be damaging. But if someone doesn’t have the skills necessary for a task and they use tools to bring their skills up to that level, then the use of that tool is meaningful and worthwhile. Let me illustrate with an example. In 2010, Liz Wiseman published the book “Multipliers” which came from a study seeking to answer the question “What are the differences between leaders who multiply intelligence among their employees and those who diminish it, and what impact do they have on the organization?” (Wiseman & McKeown, 2010). This framework of leaders who make a large positive impact on an organization is very applicable to work in schools. These multiplier leaders “liberate people from the oppressive forces” in bureaucratic educational settings (Wiseman, 2017). Whether someone multiplies those below them up (multipliers) or down (diminishers), they have a compounding impact. For the purposes of this discussion, we will focus on multipliers. They come in five categories (Wiseman &[\[HT41\]](#_msocom_41)  McKeown, 2010): ·      The Talent Magnet brings in the best people and pushes them to be their best. ·      The Liberator creates an intense environment, it’s not all sunshine and rainbows, that forces people to be their best. ·      The Challenger sets up opportunities for people to but their best foot forward and solve wicked problems (Head, 2008). ·      The Debate Maker invites rigorous and challenging scenarios where the best idea survives. ·      The Investor invests in the success of the others by giving them ownership of a problem and solution. In each of these categories, the role of the leader is imperative to helping suss[\[HT42\]](#_msocom_42) [\[JJ(S43\]](#_msocom_43)  out the skills and innovations needed to solve the problems faced in education. It’s not wise to allow the principal to be the final word on everything and think that he or she is imbued with the best ideas just because of the title of principal. That’s a recipe for disaster and stagnation.[\[HT44\]](#_msocom_44)  Rather, the multiplier acts as a lightning rod for innovation, not because they are themselves particularly innovative, but rather because they bring it out in their subordinates. Wiseman describes it like this: These Talent Magnets: “1) look for talent everywhere; 2) find people’s native genius; 3) utilize people at their fullest; and 4) remove the blockers” (Wiseman, 2017). Being an innovator is not about being the one with an idea, as we commonly think of it. It requires a multitude of skills. “Collectively, these discovery skills—the cognitive skill of associating \[linking together ideas that aren’t obviously related to produce original ideas\] and the behavioral skills of questioning, observing, networking, and experimenting—constitute what we call the innovator’s DNA, or the code for generating innovative…ideas” (Dyer et al., 2011). The cognitive skill of associating[\[HT45\]](#_msocom_45)  is an area where AI can help, but that doesn’t absolve the person of needed to use the other behavioral skills mentioned[\[HT46\]](#_msocom_46) . These previous sections serve to help us understand what AI is and where it is still lacking, and the most glaringly obvious place where the literature is lacking is in understanding what AI can do for leaders, specifically, and not just teachers. But to really understand that, we need to understand the role of the principal and how they are typically developed and trained.[\[HT47\]](#_msocom_47)  ## The Role of the Principal In 2020, I started out my book, SchoolX (Jones, 2020, p. iv), with this statement: The role of school principal may be one of the most unique positions in any organization. There aren’t many other roles that require a leader to interface with so many stakeholders, with such drastic and diverse expectations for success in different areas. The expectations from one stakeholder group often completely oppose the expectations from another group. [\[HT48\]](#_msocom_48)  As part of this review, I wanted to expand my idea of what a principal’s role is, and as I’ve been studying this topic for years using a tool called Readwise, I have been taking notes, highlighting, and saving articles, research, blog posts, and opinion for years. Also, I’ve been interviewing amazing principals around the world for my podcast for over a decade. I used the semantic search, neural networks, and natural language processing of this AI, based on the o3 model from OpenAI to come up with the following definition of what the role of the principal is: The principal is the school’s chief learning and organizational leader who, through evidence-based instructional coaching, cultivation of a safe and trusting climate, facilitation of collaborative professional learning, and strategic management of people, time, and resources, creates the conditions under which effective teaching and deep student learning flourish. This multifaceted role demands continuous professional growth, adaptive use of data and technology, and the moral commitment to serve all students—functions that research shows are best fostered through clinically rich preparation, sustained mentoring, and context-embedded coaching (_Readwise Chat_, 2025).[\[1\]](#_ftn1)[\[HT49\]](#_msocom_49) [\[JJ(S50\]](#_msocom_50)  For any principal, either of those statements reveal the hefty weight that is upon the shoulders of principals to be everything to everyone while still taking care of themselves, their families, and not forgetting even one student. Much of the literature of the last couple decades has defined the role of the principal as the instructional leader (Grissom et al., 2021; Grissom & Harrington, 2010). One area of principal leadership is management,[\[HT51\]](#_msocom_51)  and this is an area where AI can really help. AI’s administrative capacities (e.g., predictive analytics, data-informed decision making) can help lighten the burden of school leadership tasks and outline scenarios like using AI for evidence-based policy and resource allocation (Chiu et al., 2023). However, quantifying causal links remains complicated, largely because principals’ effects are indirect and mediated through other actors (Dehghani, 2025)(Hallinger & Heck, 1998). A broad empirical consensus now shows the school principal as a high-leverage actor in school improvement. Meta-analyses and longitudinal studies consistently show that principals exert school-wide effects on learning that rival, in magnitude, the contributions of individual teachers Leithwood’s ten-year review concluding that “leadership is second only to classroom instruction” (Leithwood et al., 2004, p. 5) Principals really do matter. Their impact and work has been understated, but their impact is long lasting and affects broad swaths of stakeholders (Grissom et al., 2021; School Leader Collaborative, 2023). Principal skills can be defined in four levels, warm body, manager, leader, and designer(Jones, 2020). The warm body is just there, but you wouldn’t know it if they weren’t. The manager is someone who makes sure the bells ring and the management is completed, but there is no vision, and no going above and beyond. The leader is who most of the research talks about, someone who has a vision, is an instructional leader, and works hard to help the school achieve greatness. But the designer is the one who does all of the above, and they design the school to meet the needs of the people that are there, which change every year with each new crop of teachers, students, and staff (Darling-Hammond et al., 2022). The designer is the level where the real magic in school improvement happens. After reviewing the literature over two decades, one group of researchers concluded that the investment in a designer principal could be the most impactful investment a district could possibly make in improving a school(Grissom et al., 2021). Effective principals lift a host of organizational outcomes—teacher satisfaction and retention, student engagement, attendance, and even exclusionary-discipline rates—amplifying their influence well beyond test scores (School Leader Collaborative, 2023).[\[HT52\]](#_msocom_52)  Grissom et al. (2021) break down a large body of quantitative evidence into four mutually reinforcing domains of practice: 1) instructionally focused interactions with teachers, 2) building a productive school climate, 3, facilitating collaboration and professional learning communities, and 4) personnel and resource management. That narrows it down a bit, so let’s look at these briefly. **Instructional Leadership**. Principals who engage in frequent, high-quality feedback conversations, especially where the teachers see the feedback as professional development, improve teacher effectiveness and student test scores (Garet et al., 2017; Grissom et al., 2021). However, walk-throughs are seen as much less effective in improving teacher effectiveness and student test scores (Grissom et al., 2013). Designer principals focus on coaching, evaluation, and curriculum alignment, rather than monitoring for evidences, which doesn’t help improve instruction: “Informal classroom observations or walkthroughs are more common but negatively associated with achievement gains and school improvement, at least in high schools” (Grissom et al., 2013, p. 440). Principals who are doing drive-by’s to make sure teachers are compliant, especially in high schools, does not lead to improvements. Balanced Leadership training boosted principals’ self-efficacy, yet produced no detectable change in teacher perceptions or student scores (Grissom et al., 2021). **Climate and culture building.** Studies have shown that principals design systems, processes, and opportunities to help teachers and students feel safe, valued, and supported as well as capable of achieving their individual goals (Grissom et al., 2021; Kelley & Finnigan, 2003; Louis & Murphy, 2017). When people feel safe in a school they are better equipped to learn, make good decisions, and grow personally, professionally, and academically. (Jones, 2019). Principal and school variables[\[HT53\]](#_msocom_53)  are not related to faculty trust in the principal once principal learning-centered leadership is considered. For instance, the principal’s age, tenure in administration, and time spent with faculty members do not have a significant impact on faculty trust in the principal. Similarly, school variables such as type, size, years of faculty experience, the percentage of female faculty members, and the presence of minority or poverty students at the school do not have a significant relationship with faculty trust in the principal (Farnsworth et al., 2019). In studying trust, Dr. Shane Farnsworth surveyed teachers in elementary, middle, and high school in a large district in the Rocky Mountains, and concluded that principals should take “confidence that a variable over which they have considerable control, leadership, affects trust, not variables over which they have little or no control” (Farnsworth et al., 2019, p. 24). School leaders improve the climate and culture of a building by improving trust (Bryk, 2003; Farnsworth et al., 2019; Louis & Murphy, 2017). “One of the most common dimensions of trust is vulnerability” (Farnsworth et al., 2019, p. 7), which also increases psychological safety, another key factor in improving the climate and culture of a building (Jones, 2019). **Professional Collaboration**. Principals who nurture collaboration and PLCs increase teachers’ perceptions of whether the evaluation the principals gives are legitimate or not and accelerate growth in those teachers’ instructional practice (Grissom et al., 2021). Leadership coaching, delegation, mentoring partnerships, and peer-based models also build collective efficacy, though it is true that mismatches between mentors and mentees can undermine the impact made (Hansford & Ehrich, 2006). **Strategic Resource and Personnel Management**. Principals’ self-ratings and observed skills in budgeting, scheduling, and talent decisions predict higher achievement growth, teacher satisfaction, and parent approval (Grissom et al., 2021). By ensuring that each student encounters effective teachers, principals indirectly—but powerfully—affect learning opportunities(Grissom et al., 2021). These four areas are key, but additional contemporary scholarship widens the definition to include character education leadership (Berkowitz, 2012), social-emotional leadership (Hoerr, 2017, 2022), digital leadership (Sheninger, 2019), and emerging AI-enabled instructional leadership (Bixler & Ceballos, 2025; Jones, 2025b). These strands reiterate that principals must not only manage existing systems but also lead innovation and be aware of and adept at implementing changes as they come about (Darling-Hammond et al., 2022; Master et al., 2022). Truly, the role of a principal is great, time-consuming, necessary, and requires great cognitive effort. But we still don’t completely understand how to make schools better. Perhaps one of the biggest challenges of all is directly finding cause and effect relationships between principal effectiveness and student performance. There are two main concerns (a) the intricate nature of establishing causality between student test scores and a school principal’s job performance, and (b) the considerable time interval between the leadership coaching sessions and the reports from both groups regarding their experiences (Warren & Kelsen, 2014). Ironically, Leithwood and Jantzi describe principal leadership using the same terms that some researchers use to describe AI: “Without minimizing the considerable progress that has been made over the past 15 years, however, it is safe to say that the nature of effective school leadership still remains much more of a black box than we might like to think” (Leithwood et al., 2004, p. 201). Our inability to see into the workings of a school and know what is actually making a difference is similar to the idea that AI is a black box. This effect “has earned deep neural networks a reputation of being ‘black boxes’, an apparatus whose inner-workings remain opaque to the outside observer” (Quinn et al., 2022, p. 3). Despite that, researchers still try to explain what makes great schools great. Evidence from various states and districts indicates that strong leadership policies and effective implementation lead to increased access to high-quality principal learning opportunities. In partnership with the Learning Policy Institute and the Wallace Foundation, Linda Darling-Hammond and her co-authors found a positive correlation[\[HT54\]](#_msocom_54) [\[HT55\]](#_msocom_55)  between principal preparation and professional development programs. They found that “high-quality principal preparation and professional development programs are associated with positive principal, teacher, and student outcomes, ranging from principals’ feelings of preparedness and their engagement in more effective practices to stronger teacher retention and improved student achievement” (Darling-Hammond et al., 2022, p. 6). Succeeding with these many tasks and limited time requires innovative solutions, but[\[HT56\]](#_msocom_56)  certainly today, principal preparation programs have not been teaching principals how to use AI for innovation. In order for actual change to take place, principals need opportunities to learn that are personalized to their context, using examples and experiences from their work, and provide an opportunity to see how it plays out in their day-to-day work in additional to being timely, relevant, and of value (Ceballos & Bixler, 2024; Dong et al., 2022; Master et al., 2022; Warren & Kelsen, 2014). That is just how we teach them to manage all the major aspects of their role as instructional leaders. Further scholarship is needed on the coaching aspect of this work, but that is beyond the scope of this study and review. This new age of education requires something more than just instructional leadership as shown above by Grissom et al., and that is where innovation comes in. Principals need to constantly find new solutions to new problems, which requires innovation. [\[HT57\]](#_msocom_57)  ## Innovation To start, we should get some clarity on what innovation is. There have been many definitions, but the most popular by far is Clayton Christensen’s Disruptive Innovation (previously called Disruptive Technology (Porter & Dike, 2023)), which is defined as a “smaller company with fewer resources is able to successfully challenge incumbent businesses” (Christensen et al., 2015, p. 46). Additionally, a couple key points matter, it needs to start at the low end of the price spectrum, and then moves upmarket (Christensen et al., 2015).[\[HT58\]](#_msocom_58)  While this is talking about business innovation, this same idea has been applied to nearly every business, non-profit, church, and government entity possible (Porter & Dike, 2023). One of Christensen’s notable examples features Netflix, which forced Blockbuster to eliminate late fees (which is where the majority of their revenue came from), and eventually led to their bankruptcy, because Netflix came in at the low end of the market, offering DVDs by mail which customers could keep as long as they wanted. Education authors and others have further defined Innovation. ASU defines innovation as principled: “Principled Innovation is the ability to imagine new concepts, catalyze ideas, and form new solutions, guided by principles that create positive change for humanity” (_Principled Innovation in the Systems of Educator and Leader Preparation_, 2019). Fullan et al. (2024) describe our current position as the dawn of a technology that has transitioned from a mere toy tool to a disruptive innovation. Simon Sinek (2019) in his book _The Infinite Game[\[HT59\]](#_msocom_59)_  describes organizations that play the infinite game (as opposed to a finite game) more capable of innovation. How does this idea of innovation relate to the principal and AI? With principals leading AI, principals are empowered as learners to offload complex cognitive tasks to the AI and therefore build cognitive equity (Bixler & Ceballos, 2025; Jones, 2025a). Again, much of the literature on AI focuses on instruction and AI, not AI and problem-solving. Principals can lead with AI to solve instructional leadership questions through data analysis, self-directed learning, and making decisions (Bixler & Ceballos, 2025). Truly, this type of work is what AI is most suited for right now, information retrieval and processing. Researchers studied over 100,000 conversations users had on the platform Bing to determine what activities were most used by them for this purpose. They found “the highest AI applicability scores for knowledge work occupation groups such as computer and mathematical, and office and administrative support, as well as occupations such as sales whose work activities involve providing and communicating information” (Tomlinson et al., 2025, p. 1). While there is much handwringing about AI taking over jobs, this is not what usually happens with technology (Bessen, 2015). As with the banking industry, the innovation of ATMs decreased the need for tellers, but increased their reach as they opened more branches, with fewer staff than originally planned, which ironically meant that more tellers were employed than previously. “Thanks to the ATM, the number of tellers required to operate a branch office in the average urban market fell from 20 to 13 between 1988 and 2004” (Bessen, 2015, p. 17). The reduction in the number of tellers required allowed banks to open more branches, raising the number of banks in urban by 43%, “As more ATMs were installed in the United States, the number of tellers employed did not drop” (Bessen, 2015, p. 17). This innovation is not likely to put principals out of work, nor teachers, but it is likely to _change the nature_ of their work (Bessen, 2015; Bixler & Ceballos, 2025; Fullan et al., 2024; Karakose, 2024). As with ATMs and tellers, ATMs made the role of the teller change from a cash dispenser (the job role taken over by the ATM) to “form a personal relationship with these customers can help sell them on high-margin financial services and products” or part of a “relationship banking team” (Bessen, 2015, p. 17). We can expect the role of teacher and principal to change in a similar way. The idea of a teacher being a dispenser of information has long been challenged and their role has already been shifting. Just as a principal used to be seen as a disciplinarian and manager is now seen as an instructional leader (Grissom et al., 2021; Wallace Foundation, 2009), their role will evolve as well. Once innovation happens, there is a way that innovation is adopted. This is called the Diffusion of Innovation Theory (Rogers, 2003). The Diffusion of Innovation Theory, first discussed historically in 1903 by the French sociologist Gabriel Tarde, initially depicted an S-shaped diffusion curve (Kaminski, 2011). Ryan and Gross in 1943 later introduced the concept of adopter categories, which were later incorporated into the current theory popularized by Everett Rogers (Kaminski, 2011). Katz, in 1957, also contributed to the theory by introducing the notions of opinion leaders, opinion followers, and the media’s influence on these two groups (Kaminski, 2011). The diffusion of innovation can be seen as a bell curve, with ·      Innovators (2.5%), ·      Early adopters (13.5%), ·      Early Majority (34%), ·      Late Majority (34%), ·      Laggards (13.5%), and finally ending with ·      Non-Adopters (2.5%) (Ho, 2022a, p. 365). There is a chasm between the early adopters and early majority, and there is a similar chasm between the late majority and the laggards (Ho, 2022a). These chasms, or time it takes this group to adopt (Kaminski, 2011), are the opportunities for high-end (read: expensive) innovation ((Ho, 2022a) and low end disruptive innovation (read: low cost, and non-incumbent-driven) (Christensen et al., 2015). Those chasms are particularly important because it is where the disruptive innovation happens. For school leaders, we can reasonably assume that 2.5% of the population was using AI (think Machine Learning, Automation, etc) before ChatGPT was released, and they were the people who jumped on the beta version of ChatGPT. They are also the people using other tools besides ChatGPT for AI support (Willison, 2023). As an innovator myself, I saw great potential in ChatGPT and other AI tools, and had actually been using them and thinking about them before they were broadly available to the public(Jones, 2023b). Despite these advantages, the integration of AI in education presents several challenges. As noted above, these include the necessity for extensive data to train AI systems, ongoing updates to maintain system accuracy, and the imperative to uphold ethical standards, particularly concerning student privacy and data security. Additionally, the risk of over-reliance on AI at the expense of human interaction necessitates a balanced approach that positions AI as a complementary tool rather (to use August & Tsaima’s term, exoskeleton) than a replacement for educators (AI Teacher).[\[HT60\]](#_msocom_60)  To address these considerations, the A-PLUS framework has been proposed (Jones, 2023a), emphasizing critical principles for responsible AI integration: **Accessibility**: Ensuring that AI tools are inclusive, accommodating diverse learner backgrounds, abilities, and learning styles. **Privacy and Ethics**: Upholding stringent ethical standards, safeguarding student data, and promoting transparency in AI operations. **Learner-Centricity**: Prioritizing student well-being, autonomy, and critical thinking, while using AI to support rather than supplant human guidance. **Usability**: Developing intuitive, user-friendly AI interfaces that facilitate widespread adoption and minimize technical barriers. **Sustainability**: Encouraging scalable, cost-effective AI solutions that are environmentally sustainable and adaptable to future educational advancements. This framework still leaves much to be desired as it focuses on adopting technology in schools, not on the leadership, though many of the ideas can relate. ## Innovation Framework Sonny Magana’s T3 Framework provides a critical perspective on technology integration in educational settings, offering a nuanced approach to understanding how technology can be meaningfully implemented to enhance learning (Magana, 2019). Central to the framework is the fundamental premise that educational technology tools are inherently “value neutral” - their impact depends entirely on how they are used to support, augment, or enhance instructional practices (Magana, 2019).[\[HT61\]](#_msocom_61)  The framework delineates three distinct domains of technological use, each representing a progressively more sophisticated approach to educational technology. The first domain, Translational (T1) Technology Use, is characterized by simply digitizing existing analog tasks[\[HT62\]](#_msocom_62) . This is just translating it to technology. An analog (paper) worksheet turns into a digital worksheet. At this level, technology primarily serves to increase the speed, ease, or accuracy of traditional educational activities, such as replacing a paper survey with a digital form. This represents the most basic level of technological integration, where educators are essentially doing old tasks in new ways. In contrast, Transformational (T2) Technology Use involves substantive disruptions in the nature of tasks, individual roles, or task impact. This domain represents a significant leap in educational technology implementation, where technology is used to do new things in new ways.[\[HT63\]](#_msocom_63)  For example, blogs allowed students to write for a real audience beyond the teacher. Research has shown that transformational technology use can demonstrate effect sizes around 1.6 in improving student learning, making it a particularly powerful approach to educational innovation. The most advanced domain, Transcendent (T3) Technology Use, goes beyond normal expectations of technology integration. At this level, technology enables students to create entirely new learning environments and design innovative learning tools through software coding. Transcendent technology use provides opportunities for students to achieve mastery that extends far beyond traditional learning objectives.[\[HT64\]](#_msocom_64)  Transcendent technology use is really innovative in that it provides new opportunities that weren’t even considered before. Magana argues that the fundamental challenge in educational technology is the predominance of translational (T1) use with minimal transformational (T2) or transcendent (T3) implementation. The[\[HT65\]](#_msocom_65)  framework serves multiple purposes: it necessitates meaningful digital tool integration, provides a hierarchy of technological value in learning environments, and offers a self-assessment tool for educators to evaluate and improve their technology use. The practical applications of the T3 Framework are wide-ranging. Principals can use it to evaluate instructional technology use, instructional coaches can develop targeted professional development, educational leaders can guide technology purchasing decisions, and teachers can set meaningful technology integration goals.[\[HT66\]](#_msocom_66)  At its core, the framework embodies the philosophical principle that “good teaching is the melody, and good technology integration adds the harmony” (Magana, 2019) It challenges educators to move beyond mere digitization and instead use technology to fundamentally transform learning experiences. By providing a structured approach to understanding technological innovation, the T3 Framework offers educational leaders a powerful lens for reimagining how innovation and technology can be leveraged to[\[HT67\]](#_msocom_67)  enhance learning outcomes. ## The Need for a New Framework in Educational Leadership While Magana’s T3 Framework provides valuable insights for classroom technology integration, its application to educational leadership in the age of AI reveals significant limitations. The framework, though robust for teacher-level technology implementation, falls short in addressing the complex organizational challenges that principals face when leading AI integration across entire school systems. The first major limitation stems from the framework’s focus on classroom-level implementation rather than organizational transformation. While T3 effectively categorizes technology use as Translational, Transformational, and Transcendent at the instructional level, principals’ highest-leverage work involves organizational elements such as culture building, resource allocation, data governance, and stakeholder trust-building (Grissom et al., 2021). These system-level necessities remain largely unaddressed within the T3 Framework. Furthermore, the emergence of AI has fundamentally altered the instructional leadership landscape. As highlighted by recent research on innovation diffusion (Ho, 2022b), technological changes typically don’t eliminate jobs but rather shift skill requirements and create new capabilities. In education, this means that generative AI can now produce lesson plans, feedback rubrics, and item analyses that rival or exceed what most principals traditionally provide during classroom walk-throughs. This technological advancement has effectively collapsed the instructional advice gap, shifting principals’ comparative advantage away from “instructional coaching one teacher at a time” toward “designing the ecosystem in which AI and people thrive.” The organizational leadership challenges created by AI extend “beyond T3” into what Rittel and Webber (1973) would classify as “wicked problems” - complex challenges characterized by incomplete, contradictory, and changing requirements. Principals now face tasks such as: - Developing ethical data policies that address privacy, bias, and model transparency - Redesigning workflows to accommodate AI-human collaboration - Creating talent strategies for re-skilling and re-assigning staff - Managing infrastructure procurement and ROI analysis - Building community trust through effective change management[\[HT68\]](#_msocom_68)  None of these critical organizational challenges appear in T3’s teacher-focused framework. The economic implications of AI adoption in education mirror trends observed in other fields. As noted in the International Monetary Fund’s “Jobs on the Line” report (Bessen, 2015), technological advancement tends to widen the gap between median and elite performers. Just as top designers capture disproportionate value because their work scales effectively, “Designer Principals” who can effectively leverage AI for innovative organizational[\[HT69\]](#_msocom_69) [\[HT70\]](#_msocom_70)  transformation will likely generate substantially more value than those who merely monitor existing systems. This evolving landscape calls for a post-T3 framework that addresses: 1.     Governance structures for data, ethics, and security 2.     Ecosystem design incorporating AI-enabled workflows and professional learning 3.     Strategic portfolio management of AI tools 4.     Cultural development supporting innovation and experimentation The limitations of T3 in addressing these needs reflect a broader challenge in educational innovation: the tendency to focus on classroom-level implementation while overlooking systemic transformation. As Christensen’s theory of disruptive innovation suggests, true transformation often requires fundamentally new approaches rather than incremental improvements to existing frameworks (Christensen et al., 2015). [\[HT71\]](#_msocom_71)  Moving forward, educational leadership frameworks must evolve beyond the classroom-centric view of technology integration to address the organizational complexities of AI implementation. This evolution requires recognizing AI integration as a wicked problem requiring adaptive, systemic solutions rather than purely technical ones. The next generation of frameworks must help principals navigate both the technical and adaptive challenges of creating AI-enabled learning organizations while maintaining focus on ethics and not only educational outcomes, but also outcomes that are more difficult to measure than test scores. # References American Federation of School Administrators. (2023, June 26). _Artificial Intelligence and Education_. https://www.theschoolleader.org/news/artificial-intelligence-and-education Ari, A. (2025). Implementation of Artificial Intelligence and the Roles of Educational Leadership: Investigating the Expectations of Kindergartens’ Principals. _International Journal of Instruction_. August, S. E., & Tsaima, A. (2021). Artificial Intelligence and Machine Learning: An Instructor’s Exoskeleton in the Future of Education. In _Innovative Learning Envronments in STEM_ (pp. 79–105). Springer International Publishing. https://doi.org/10.1007/978-3-030-58948-6\_5 Barr, A. (2025, April 25). ChatGPT is crushing Google in the AI race. Unless you look at the data differently. _Business Insider_. Berkowitz, M. W. (2012). Moral and character education. In K. R. Harris, S. Graham, T. Urdan, S. Graham, J. M. Royer, & M. Zeidner (Eds.), _APA educational psychology handbook, Vol 2: Individual differences and cultural and contextual factors._ (pp. 247–264). American Psychological Association. https://doi.org/10.1037/13274-010 Berkowitz, M. W. (2021). _PRIMED for character education: Six design principles for school improvement_ (Kindle ed). Routledge. Bessen, J. (2015). Toil and Technology. _Finance & Development_, _52_(1), 1. https://doi.org/10.5089/9781498351942.022 Bixler, K., & Ceballos, M. (2025). _Principals Leading AI in Schools for Instructional Leadership: A Conceptual Model for Principal AI U_. Bryk, A. S. 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(2017). _Multipliers: How the best leaders make everyone smarter_ (Revised and updated edition). HarperBusiness, an imprint of HarperCollinsPublishers. Wiseman, L., & McKeown, G. (2010). Bringing Out the Best in Your People. _Harvard Business Review_. https://hbr.org/search?term=R1005K * * * [\[1\]](#_ftnref1) The jury is still out on citing AI in academic research. Adding an appendix with the AI conversation is silly. Linking to all AI “chats” is not always possible, as in this case. Current best practices among scholars in academic forums suggest citing it as software, as I have done here, and explaining as much as is reasonable the process by which you used AI. As the technology gets increasingly baked into the systems we use daily, the likelihood of knowing what model was used, who the programmer of it is, and other questions typically pertinent to an academic pursuit, are greatly reduced. Furthermore, you wouldn’t cite Grammarly for each time it corrected your spelling, would you? AI is being baked into the tools we use more and in more invisible ways, and I’m lucky I was able to cite as much as I did from Readwise Chat. This topic alone could certainly warrant a long research paper of it’s own, which I was tempted to pursue, but told myself to focus on the task at hand. * * *  [\[HT1\]](#_msoanchor_1)GOOD job, Jethro. I enjoyed reading this. As you'll see, I think you need to firm up your inferences with some quotes and I pushed on using the word "innovation," but this is very good progress.  [\[HT2\]](#_msoanchor_2)Well positioned and said.  [\[HT3\]](#_msoanchor_3)As noted previously, I would like at least one quote from one of these authors that evidences the case you are making. Listing them all is fine, but without a quote the reader is only left with your judgment of their thoughts.  [\[HT4\]](#_msoanchor_4)1\. Jones' comment seems like a good place for his/her quote. 2. You say "people". would it be helpful to specific "children and adults" or some other way to note that you mean both groups?  [\[HT5\]](#_msoanchor_5)Should "innovation" be plural? I'm not sure.  [\[JJ(S6\]](#_msoanchor_6)No, because types is plural.  [\[HT7\]](#_msoanchor_7)A quote is definitely needed here (two make your case even stronger).  [\[HT8\]](#_msoanchor_8)Does the16.3% mean that 83.7% are not teaching students how to use it? If so, while problematic, how does that tie to your focus?  [\[HT9\]](#_msoanchor_9)Since it's your theme, is there a way to rephrase this sentence, this opening, and include the word "innovate"? That is what they're doing yet you don't mention it (and fail to return to your focus).  [\[HT10\]](#_msoanchor_10)Jethro, well done! You've worked hard at this and it flows well.  [\[HT11\]](#_msoanchor_11)Quotation marks needed.  [\[HT12\]](#_msoanchor_12)A quote here would be good, and I'm wondering if the quote would evidence, by its absence, your point about the too low expectations for AI use.  [\[HT13\]](#_msoanchor_13)Can you add a comment/quote about AI use, i.e., how many millions in X time period?  [\[HT14\]](#_msoanchor_14)This should be rewritten so the same reference doesn't appear three times in the same para, with no specific quote.  [\[HT15\]](#_msoanchor_15)AI  [\[HT16\]](#_msoanchor_16)Again, I need to see a quote from one or both.  [\[HT17\]](#_msoanchor_17)An example of a wrong prediction would be good.  [\[HT18\]](#_msoanchor_18)Good analysis, Jethro. Also, it may be stronger if you add a bit, e.g.: "They are missing a key piece of problem solving and innovation, how AI might be used to approach the role of the principal differently" or "... to address unidentified problems." BUT that is my idea and it's your dissertation, so your call.  [\[HT19\]](#_msoanchor_19)Under-covered sounds like it's from a spy novel.  [\[HT20\]](#_msoanchor_20)Good, this quote means I don't have a question.  [\[HT21\]](#_msoanchor_21)This sounds like a quote, yes?  [\[HT22\]](#_msoanchor_22)I'm curious, how do you react to this approach?  [\[JJ(S23\]](#_msoanchor_23)I like this approach, personally, but is it appropriate for me to endorse this in the literature review?  [\[HT24\]](#_msoanchor_24)A quote is needed here.  [\[HT25\]](#_msoanchor_25)Never mind. While it would be helpful, I see some later.  [\[HT26\]](#_msoanchor_26)exit? Exist in?  [\[JJ(S27\]](#_msoanchor_27)no, that should be exit. As in, in order for learning to matter beyond taking the test, we can't just give people tools.  [\[HT28\]](#_msoanchor_28)I think you need to include innovation here.  [\[HT29\]](#_msoanchor_29)If I'm correct in that you're pushing against their point, better to begin with their thinking (a quote) and then put in your "Yes, but..."  [\[HT30\]](#_msoanchor_30)Excellent point. I think this needs a bit more text and surely there's quote or two that speaks to it.  [\[HT31\]](#_msoanchor_31)Please clarify.  [\[HT32\]](#_msoanchor_32)I'm still a bit unclear even after reading the para.  [\[HT33\]](#_msoanchor_33)I've been seeing articles about how AI can replicate - evidence - empathy and this potential is scary. Can you expand (and include a quote)?  [\[HT34\]](#_msoanchor_34)but?  [\[HT35\]](#_msoanchor_35)So this is a term that you created?  [\[JJ(S36\]](#_msoanchor_36)Yes.  [\[HT37\]](#_msoanchor_37)Sorry, Jethro, my problem but I didn't follow this. Please rephrase.  [\[HT38\]](#_msoanchor_38)Good. You explain it as CAN, but I think you need to rephrase your definition of your term, so it is an "is" statement.  [\[HT39\]](#_msoanchor_39)Is it fair to say that cognitive debt reflects a reliance and, thus, is a negative?  [\[HT40\]](#_msoanchor_40)where there wasn't a voice before  [\[HT41\]](#_msoanchor_41)Interesting!  [\[HT42\]](#_msoanchor_42)suss?  [\[JJ(S43\]](#_msoanchor_43)to inspect or investigate (something) in order to gain more knowledge [https://www.merriam-webster.com/dictionary/suss%20out](https://www.merriam-webster.com/dictionary/suss%20out)  [\[HT44\]](#_msoanchor_44)Agree and you need a quote to support this claim (which should be easy to get).  [\[HT45\]](#_msoanchor_45)with others ?  [\[HT46\]](#_msoanchor_46)Can you remind us of them?  [\[HT47\]](#_msoanchor_47)Good segue.  [\[HT48\]](#_msoanchor_48)Even though it's you, you need an official citation.  [\[HT49\]](#_msoanchor_49)I've not gotten to your list of references, but in my books, ASCD has me include the actual link.  [\[JJ(S50\]](#_msoanchor_50)Including the link is not possible with this tool and will likely not be possible with many tools coming out in the future.  [\[HT51\]](#_msoanchor_51)Would it help to have a simple graphic? (I have one from a survey I did, for example, in which teachers said what they wanted from principals. The data fell into managerial, instructional support, and relational. (No surprise to us, relational was noted many, many more times.)  [\[HT52\]](#_msoanchor_52)I like this a lot. But I wonder how interviewing panels would view it.  [\[HT53\]](#_msoanchor_53)Is demographics a better term?  [\[HT54\]](#_msoanchor_54)positive  [\[HT55\]](#_msoanchor_55)"... positive correlation between \_\_\_\_\_\_ and \_\_\_\_. They found that..."  [\[HT56\]](#_msoanchor_56)Your call, Jethro, but this is an abrupt transition to innovation. I think you need to include something like "Succeeding with these many tasks and limited time requires innovative solutions, but certainly today..."  [\[HT57\]](#_msoanchor_57)Given your thrust, this last sentence cries for the word innovation.  [\[HT58\]](#_msoanchor_58)Any business examples that the reader would recognize?  [\[HT59\]](#_msoanchor_59)Italicize or quote book titles (articles too).  [\[HT60\]](#_msoanchor_60)Appropriate to use "cognitive debt" here?  [\[HT61\]](#_msoanchor_61)Similarly, Garder refers to intelligences as amoral.  [\[HT62\]](#_msoanchor_62)Please explain analog or use another term.  [\[HT63\]](#_msoanchor_63)An example?  [\[HT64\]](#_msoanchor_64)Shouldn't "innovative" be used here? That IS your focus.  [\[HT65\]](#_msoanchor_65)Is this educational technology without innovation?  [\[HT66\]](#_msoanchor_66)Again, Jethro, as I read this section I keep waiting for you to note how remaining as the Translation phase precludes innovation, etc.   [\[HT67\]](#_msoanchor_67)use innovation to   ???  [\[HT68\]](#_msoanchor_68)Should there be a reference to innovation?  [\[HT69\]](#_msoanchor_69)For innovative organizational transformation? ( recognize that this may feel redundant; however, given your thrust, I think the word innovate or innovative need to be present.  You're talking about innovatono, not T3.  [\[HT70\]](#_msoanchor_70)Oops, innovation, not T3 (although that is innovation).  [\[HT71\]](#_msoanchor_71)Strong para!