Educational Technology and Change Journal

By Jim Shimabukuro (assisted by Perplexity)
Editor

The three best uses of AI in education in 2026, judged by convergence across recent systematic reviews, major policy guidance (UNESCO, OECD) and large‑scale survey/analytic work, are: first, AI‑driven personalized tutoring and adaptive learning; second, AI‑supported assessment and feedback for learning; and third, AI as an assistant that offloads teachers’ routine workload so they can focus on higher‑value human work.^1-8 These uses recur at the top of expert syntheses as the clearest cases where AI capabilities align with robust evidence of learning gains, better formative information, and improved teaching conditions, while remaining compatible with ethical, human‑centered principles.^1,2,5,6

The selection criteria are threefold. First, each use must appear across multiple high‑quality syntheses between 2024 and early 2026—such as systematic reviews of AI in education, scoping reviews of generative AI in higher education, or major policy research by bodies like UNESCO and the OECD—rather than relying on a single enthusiastic study.^1,2,4,7,8 Second, there must be evidence of positive impact on core educational outcomes: student learning, engagement, equity, or teacher effectiveness, with at least quasi‑experimental or well‑documented field data rather than purely speculative claims.^1,3,4,6 Third, leading policy and governance documents must judge the use as ethically acceptable and strategically important, emphasizing human agency, equity, and long‑term system goals, rather than simply efficiency or novelty.^2,5,7 These criteria tilt the ranking toward applications that are both pedagogically powerful and broadly endorsed across research, practice, and policy.

1. AI‑driven personalized tutoring and adaptive learning

Across recent reviews and policy reports, expert consensus is strongest around AI’s role in providing personalized tutoring and adaptive learning pathways for students. A 2024 systematic literature review of AI in higher education assessment, for example, identifies intelligent tutoring and personalized learning as one of the dominant clusters of real‑world AI applications, emphasizing high‑quality, real‑time, individualized feedback that supports cognitive and metacognitive skills.¹ A 2025 systematic review focusing on AI‑driven intelligent tutoring systems (ITS) in K‑12 synthesizes 28 quasi‑experimental studies with 4,597 students, finding generally positive effects of ITS on learning and performance, with statistically significant gains in pre‑post learning outcomes compared with conventional methods, though effect sizes vary and novelty effects remain a concern.³

UNESCO’s global guidance on generative AI in education explicitly highlights adaptive tutoring and personalized content generation as promising when embedded in human‑centered pedagogy and aligned with principles of equity, safety, and human agency.^2,5 The OECD’s 2025 work on AI and education similarly notes that AI capabilities are beginning to exceed human performance in some domains (for example, reading and mathematics tasks), and argues that adaptive and intelligent systems can help match instruction to learners’ profiles, provided that broader aims of education and human skills are kept in view.⁷

This use meets the selection criteria in a particularly compelling way. It has robust empirical backing: the 2025 tutoring review reports consistent positive learning effects for ITS across multiple contexts, especially when systems provide progress information and encourage student reflection on their own learning, indicating that AI‑driven adaptivity can enhance both performance and metacognition.³ A broader 2024 systematic review of AI‑supported assessment similarly finds that AI tools offering personalized feedback in tutoring‑like settings foster improved cognitive, metacognitive, and affective outcomes, including gains in critical thinking and positive emotions.^1,2

In parallel, a 2025 systematic review of generative AI in higher education documents that many implementations with students use conversational AI as a kind of on‑demand tutor: a flexible assistant for explanation, problem‑solving, and idea generation, which students report using to support self‑regulated learning.² Policy guidance from UNESCO and OECD explicitly frames these systems not as replacements for teachers but as complements that can provide individualized support, especially in under‑resourced settings facing teacher shortages.^2,5,7 This explicit endorsement on both effectiveness and ethics grounds makes personalized AI tutoring the strongest candidate for the top rank.

The reason this application matters is that it directly addresses one of education’s most stubborn structural problems: the difficulty of providing sustained, responsive one‑to‑one tutoring for every learner. Classic studies of human tutoring showed large effect sizes for individualized instruction, but the cost and staffing demands made such models largely unattainable at scale; AI‑driven tutoring systems, while still imperfect, offer a scalable approximation of continuous, low‑stakes help that students can access whenever they are stuck.³ In practice, intelligent tutoring can supply immediate feedback, hints, and alternative explanations, reducing the time learners spend in unproductive confusion and allowing teachers to concentrate their attention where human judgment and relational work are irreplaceable.^1,3

UNESCO’s guidance stresses that when such systems are designed to protect human agency and avoid bias, they can extend high‑quality learning opportunities to marginalized learners, multilingual students, and those with disabilities, who are often underserved by traditional one‑size‑fits‑all approaches.^2,5 By making individualized practice, explanation, and formative feedback widely available, AI‑driven tutoring and adaptive learning hold out the possibility of narrowing opportunity gaps while also forcing educators to rethink curricula around deeper skills such as critical thinking, collaboration, and creativity that remain uniquely human.⁷ In short, this use of AI matters because it can democratize something that has long been both powerful and scarce: sustained, responsive, tailored instructional support for each learner.

2. AI‑supported assessment and feedback for learning

The second major use that emerges from 2024–2026 work is AI’s role in assessment and feedback—using AI to generate, analyze, and respond to student work in ways that support learning rather than merely grading. A 2024 systematic review of AI‑assisted assessment in higher education synthesized 81 empirical studies and found that AI is being used for automated assessment and feedback, learning analytics and prediction, and educational chat assistants, with a recurring pattern: AI tools deliver high‑quality, real‑time, personalized feedback that improves students’ cognitive and metacognitive skills and fosters positive emotional responses.¹

Similarly, a 2025 review of AI for assessment in higher education emphasizes that generative AI is increasingly integrated into feedback pipelines, helping instructors provide richer, more timely comments and enabling personalized, adaptive assessment environments.⁴ Within the broader 2025 systematic review of generative AI in university classrooms, numerous studies show that structured use of tools like ChatGPT for in‑class tasks can promote critical thinking, reflective skepticism, and self‑regulated learning, especially when AI‑generated feedback is combined with explicit instruction on critique and reflection.² The UNESCO guidance documents also devote substantial attention to assessment, urging systems to validate generative AI tools not only for accuracy and fairness but also for their pedagogical appropriateness, and pointing to potential uses in formative assessment, feedback, and research supervision.^2,5

This application satisfies the selection criteria by combining wide adoption, positive empirical signals, and explicit policy guidance. In the 2024 assessment review, AI‑supported feedback stands out as one of the most mature and impactful uses, with multiple studies reporting improved learning outcomes and better student engagement when learners receive immediate, individualized guidance on their work.^1,4 For example, studies summarized in these reviews describe AI systems that track learners’ trajectories and provide tailored hints or suggestions, which in turn help instructors adjust instruction to individual needs; some evidence suggests that such systems can enhance both academic achievement and holistic development when implemented thoughtfully.^1,4

The generative‑AI‑in‑higher‑education review documents that when students are guided to use AI to draft, revise, and reflect on written work, they often show improvements in critical thinking behaviors, metacognitive awareness, and collaborative engagement with peers.² UNESCO’s guidance explicitly lists the creative use of generative AI in assessment and feedback as a promising area, provided that systems remain transparent, protect academic integrity, and are embedded in frameworks that maintain human oversight and responsibility.^2,5 Because of this alignment across empirical studies and normative frameworks, AI‑supported assessment and feedback clearly emerge as a top‑tier use even if, relative to tutoring, the direct learning‑gain evidence is somewhat more heterogeneous.^1,2,4

The importance of this application lies in how it can shift assessment from a high‑stakes, infrequent event into a continuous, formative process that supports learning. Traditional assessment often arrives too late and too sparsely to meaningfully shape how students study; AI tools can instead provide immediate commentary on drafts, problem‑solving steps, or practice attempts, turning every task into an opportunity for feedback.^1,4 When AI helps teachers generate rubrics, sample comments, or preliminary marks, instructors can spend more time in dialogue with students about how to interpret and act on feedback, enriching the reflective dimension of learning.^2,4

In addition, learning‑analytics‑driven assessment can reveal patterns in student misunderstandings that are hard to see from occasional tests, allowing better targeted instruction and early interventions.^1,4 However, UNESCO and OECD both stress that this potential is ethically fragile: poorly designed AI assessment can entrench bias, encourage superficial optimization, or erode trust in grading.^2,5,7 That is precisely why, in expert discussions, AI‑supported assessment is framed not as autonomous judgment but as an assistant that augments human evaluative capacity and deepens formative feedback processes. Done well, it matters because it can help educators realize long‑standing aspirations for assessment that actually improves learning, rather than merely certifying it.

3. AI as a teacher assistant to reduce workload and enhance teaching

The third major use supported by recent research and policy work is AI as an assistant that takes over routine, non‑instructional parts of teaching—planning, drafting materials, basic grading, communication—so that educators can devote more time and energy to high‑value human work with students. A 2026 Brookings analysis of AI’s future for students underscores that AI can reduce the time teachers spend on numerous teaching‑related tasks, enabling them to focus more on individualized student attention and the improvement of curriculum and instruction.⁶ The piece describes AI’s role in helping teachers create more objective and targeted forms of assessment, manage administrative burdens, and support differentiated instruction, especially in systems facing teacher shortages.⁶

Gallup’s early‑2026 survey of U.S. teachers reports that roughly three in ten teachers already use AI weekly, and that those who do save the equivalent of about six weeks per year in time—suggesting that AI assistants are beginning to materially alter workload patterns.⁸ Complementary analyses of AI and teacher burnout note that AI tools can shoulder up to 40% of non‑instructional work, with districts reporting average savings of nearly six hours per week per teacher when AI is used for lesson planning, grading, and routine communication.⁶ These practice‑oriented findings echo broader evidence in systematic reviews that AI tools help teachers innovate in planning and assessment while improving efficiency; for example, one study summarized in the 2025 generative‑AI review reports that using ChatGPT‑4 for preliminary marking reduced average marking time per poetry assignment from 30 minutes to 10 minutes without loss of quality.²

This teacher‑assistant use meets the selection criteria in a somewhat different way from tutoring and assessment but still qualifies strongly. Its direct impact on student learning is more indirect, which is why it ranks below the first two uses, yet the convergence of evidence on time savings and improved teaching behaviors is notable.^2,6,8 The 2025 systematic review of generative AI in university classrooms finds that AI helps instructors plan lessons and innovate teaching methods, moving from viewing generative AI as a direct lesson plan generator to using it as a tool to refine existing plans and design classroom activities; this shift accompanies deeper, more elaborated planning behavior among teachers.²

Survey data synthesized in a Microsoft 2025 special report and similar analyses suggest that well over half of educators now use AI in some capacity, with many reporting improved ability to differentiate instruction and manage classes.⁵ The Brookings commentary emphasizes that when AI automates low‑value tasks, teachers can invest more time in formative interactions, mentoring, and the social‑emotional work of teaching—activities that neither current nor foreseeable AI can replicate.⁶ UNESCO’s guidance documents similarly stress that AI should be used to empower teachers, not displace them, and should be paired with capacity‑building and professional development.^2,5 This alignment between empirical time‑saving data, observed shifts in teaching practice, and policy insistence on human‑centered design justifies its place among the top three uses.

The significance of this application is systemic: it speaks to the sustainability and quality of teaching itself in an era of mounting pressures and shortages. Many school systems report chronic teacher burnout, increased administrative demands, and difficulty recruiting and retaining qualified educators; under such conditions, any technology that can meaningfully reduce routine workload without eroding professional judgment becomes strategically important.^6,8 When AI drafts initial lesson plans, generates differentiated materials, or performs first‑pass grading, it can free teachers to do what only they can do—build relationships, orchestrate rich discussions, interpret nuanced student needs, and cultivate classroom cultures of curiosity and care.^2,6

At a system level, this can help mitigate inequalities by enabling teachers in under‑resourced schools to access sophisticated planning and assessment supports that previously required extensive time or specialized training.^5,6 The OECD’s focus on AI’s broader implications for human skills and the goals of education suggests that empowering teachers in this way is not a side benefit but central: if AI is reshaping what it means to be skilled and employable, then teachers themselves must have the time and tools to reimagine curricula, not simply cope with administrative overload.⁷ Thus, AI as a teacher assistant matters because it addresses the human infrastructure of education—teachers’ time, energy, and professional agency—upon which all other AI innovations ultimately depend.

References

1. “AI‑assisted assessment in higher education: A systematic review” (Journal of Educational Technology and Innovation, 2024). https://www.jeti.thewsu.org/index.php/cieti/article/view/209
2. “A Systematic Review of Responses, Attitudes, and Utilization of Generative AI in Higher Education Classrooms” (Frontiers in Psychology, 2025). https://pmc.ncbi.nlm.nih.gov/articles/PMC12023922/
3. “A systematic review of AI‑driven intelligent tutoring systems (ITS) in K‑12 education” (Heliyon, 2025). https://pmc.ncbi.nlm.nih.gov/articles/PMC12078640/
4. “Utilizing artificial intelligence for assessment in higher education” (Journal of University Teaching & Learning Practice, 2025). https://files.eric.ed.gov/fulltext/EJ1484420.pdf
5. “Artificial intelligence in education: A systematic literature review” (Expert Systems with Applications, 2024). https://www.sciencedirect.com/science/article/pii/S0957417424010339
6. “AI’s future for students is in our hands” (Brookings Institution, 2026). https://www.brookings.edu/articles/ais-future-for-students-is-in-our-hands/
7. “Artificial intelligence and education and skills” (OECD, 2025). https://www.oecd.org/en/topics/sub-issues/artificial-intelligence-and-education-and-skills.html
8. “Three in 10 Teachers Use AI Weekly, Saving Six Weeks a Year” (Gallup, 2026). https://news.gallup.com/poll/691967/three-teachers-weekly-saving-six-weeks-year.aspx
9. “Guidance for generative AI in education and research” (UNESCO, guidance page, 2025). https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research
10. “Artificial intelligence in education – AI” (UNESCO AI in Education portal, 2025). https://www.unesco.org/en/digital-education/artificial-intelligence

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