Yes, AI tools can explain complex textbook chapters better than teachers in some tightly defined situations, especially when a student needs an idea rephrased repeatedly, personalised to prior knowledge, converted into another format or tested immediately; but current evidence does not support replacing teachers, because explanation quality is only one part of teaching. The sharpest 2026 finding is not that machines have beaten classrooms. It is that carefully designed AI tutoring can sometimes beat a single classroom explanation while still depending on the same learning science, curricular boundaries and human judgement that good teachers provide.
That distinction matters because students are already making the comparison in practice. The Higher Education Policy Institute’s 2026 survey of 1,054 full-time UK undergraduates found that 95% used AI in at least one way and 49% believed it had improved their student experience, particularly through time savings, better understanding and instant support. Yet the same report captured the opposite experience too: students worried about skill erosion, fairness and becoming dependent on AI. The question is therefore no longer whether AI can make a dense chapter easier to read. It can. The harder question is whether the explanation produces transferable understanding after the chat window closes.
This article separates five things that ranking pages often blur together: explaining a passage, tutoring a learner, teaching a curriculum, motivating sustained effort and evaluating whether understanding is genuine. It reviews randomised and quasi-experimental evidence from 2025–2026, compares common AI study tools, identifies where teachers still have a structural advantage, and gives a chapter-by-chapter workflow that preserves the useful friction of learning instead of automating it away.
What Does “Better” Actually Mean?
Most comparisons fail before they begin because “better than a teacher” is treated as one variable. It is at least five. An explanation can be clearer, faster, more personalised, more engaging or more accurate, and those properties can point in different directions.
AI has an obvious mechanical advantage in re-expression. A student can ask for the same paragraph as an analogy, a causal chain, a visual outline, a worked example, a glossary, a quiz or an explanation pitched at three levels of difficulty. A teacher can do all of those things too, but not for thirty students simultaneously and not at 1:20 a.m. That makes AI unusually strong at the moment after a student thinks, “I read the page twice and still do not get it.”
Teaching, however, includes diagnosis. A good teacher notices that the student who says “I understand” is using the right vocabulary with the wrong mental model. The teacher knows what the class has covered, what the exam rewards, which misconception is common, whether the student is avoiding effort, and when an apparently simple question signals a deeper gap. General-purpose AI sees only the context it is given and can mistake a fluent conversation for mastery.
That leads to a more useful test: an AI explanation is better only if the student can subsequently retrieve, apply and discriminate the concept without the AI present. If a chapter feels easier but later performance does not improve, the tool improved comfort, not learning.
| Job | AI Advantage | Teacher Advantage | Best Measure |
| Rephrase a dense passage | Instant variants; unlimited repetition | Knows class context and prior misconceptions | Can the student restate it accurately? |
| Teach a new concept | Adaptive examples and pacing | Sequences curriculum and notices confusion | Can the student solve a new problem? |
| Correct an error | Immediate, private feedback | Distinguishes slip, misconception and disengagement | Does the same error recur? |
| Motivate persistence | Always available; non-judgemental | Relationship, accountability and social meaning | Does the student keep practising? |
| Judge mastery | Fast question generation and feedback | Professional judgement and standards alignment | Performance without assistance |
What the 2025–2026 Evidence Actually Shows
The strongest case for AI comes from interventions that look less like a generic chatbot and more like deliberately engineered tutoring. In a 2025 Scientific Reports randomised controlled trial led by Harvard researchers Greg Kestin and Kelly Miller, college students using a custom AI tutor learned significantly more in less time than students in an in-class active-learning condition. The key caveat is crucial: the tutor was designed around the same evidence-based pedagogy used in the classroom. The result supports good AI tutoring design; it does not prove that opening a blank chatbot and asking “explain chapter six” will outperform a teacher.
Google Research’s Learn Your Way experiment pushes the question closer to textbook chapters. The system transformed source material into personalised immersive text, quizzes, slides with narration, audio lessons and mind maps. Google reported that students using the system scored 9% higher on an immediate assessment and 11 percentage points higher on a retention assessment three to five days later than students using a standard digital reader. This is evidence that changing representation can improve learning from textbook material, not simply make it more pleasant.
Recent 2026 work also shows the importance of structure. A Scientific Reports study of 97 Grade 12 male mathematics students in four Iranian public high schools found that students receiving AI-supported instruction through Edmentum Exact Path outperformed conventional teacher-led instruction on immediate achievement, delayed retention and engagement. The authors explicitly framed the system as supplementary and warned against broad generalisation from one population.
A separate August 2026 NBER field experiment involving more than 6,000 middle-school students is even more useful for the mechanism. AI support improved next-attempt correctness after mistakes and reduced the attempts needed to return to a correct answer, but AI students progressed more slowly and attempted fewer questions. That is not a contradiction. Productive tutoring can deliberately slow a learner down.
| Study | Population | Comparison | Headline Result | Important Limitation |
| Kestin et al., 2025 | College physics students | Custom AI tutor vs active-learning class | More learning in less time with AI tutor | Purpose-built tutor; not generic chatbot use |
| Google Learn Your Way, 2025 | US students in textbook-learning study | AI-transformed material vs digital reader | 11-point retention advantage reported | Research prototype and specific content |
| Ismail et al., 2026 | 97 Grade 12 male maths students in Iran | AI-supported vs conventional instruction | Higher achievement, retention and engagement | Small, single-country, non-random individual assignment |
| Oreopoulos et al., 2026 | 6,000+ middle-school students | AI vs computer-assisted learning support | Better recovery after errors; slower progression | Working paper; focused maths-practice setting |
For a broader tool-level view, the magazine’s AI tools for students guide is useful precisely because it separates research, explanation, writing and revision instead of treating one chatbot as the best choice for every academic task.
Why AI Can Out-Explain a Static Textbook Page
A textbook has to commit to one sequence and a small number of examples. AI does not. That difference creates genuine information gain for a learner who is stuck on one chapter.
First, AI can change granularity on demand. A paragraph on oxidative phosphorylation can become a one-sentence overview, a step-by-step mechanism, a comparison table, an analogy to a battery, or a set of questions that reveal where the learner’s chain of reasoning breaks. The pedagogical value is not the novelty of the analogy; it is the ability to move up and down the abstraction ladder without waiting for another lesson.
Second, AI can make prerequisites visible. A textbook chapter often assumes knowledge from earlier chapters. A student may blame the new concept when the real problem is an old gap. A well-prompted AI tutor can ask diagnostic questions, identify the missing prerequisite and temporarily route the learner backward before returning to the chapter.
Third, multimodal transformation matters. Google’s Learn Your Way work is important because it did not merely summarise. It generated multiple representations around the same source: quizzes, audio, slides and mind maps. That is closer to how teachers naturally vary explanation when one route fails.
Fourth, AI reduces the social cost of repetition. Students often avoid saying “I still don’t understand” for the fourth time. A machine does not become impatient. This matters most for misconceptions that need repeated reconstruction rather than one polished explanation.
The catch is grounding. The more an AI is allowed to free-associate beyond the textbook, the greater the chance that it introduces notation, terminology or claims that do not match the course. The best chapter explanation is often not the broadest answer; it is the explanation that stays tightly attached to the assigned source.
That source-first principle is also why a ChatGPT study-guide workflow should begin with the syllabus and assigned materials rather than a request for a generic summary.
Where Teachers Still Have the Structural Advantage
Teachers are not just explanation engines with lower bandwidth. Their advantage appears when the learning problem is ambiguous.
A teacher can read silence, hesitation, overconfidence and peer dynamics. Those signals do not appear in a pasted chapter. Teachers also know what “correct” means locally. A chemistry lecturer may insist on one convention, a law professor may expect a jurisdiction-specific interpretation, and a history teacher may grade the quality of evidence rather than the neatness of the summary. AI can approximate those expectations only after they are supplied.
Teachers also control sequencing. A student may want the fastest answer to chapter nine, while the teacher knows the class needs to wrestle with chapter eight first. That friction can look inefficient in the moment and still be pedagogically necessary.
Sal Khan made the same point in July 2026 while arguing against giving technology too much credit: “The teachers and administrators deserve most of the credit.” His warning is operational, not sentimental. High-performing technology-enabled schools also depend on implementation, routines, leadership and instructional choices that are easy to erase when outcomes are attributed to the software alone.
Dr Kristen DiCerbo, Khan Academy’s Chief Learning Officer, offered an equally useful test in January 2026: “The provider should be able to provide evidence that learning on the platform transfers to improved results on other assessments.” That is the standard students should apply to themselves too. If AI only improves performance while AI is present, the learning claim is weak.
Teachers therefore remain strongest at choosing the goal, interpreting messy evidence of understanding, protecting standards and creating the social conditions that make a learner persist. AI is strongest at serving the next explanation or practice opportunity inside that larger design.
Students using general assistants should keep that division explicit; the magazine’s ChatGPT for students guide treats the model as a learning aid for explanation, planning and feedback rather than an authority that replaces course requirements.
Which Textbook Chapters Benefit Most From AI Explanation?
AI’s advantage is not evenly distributed across subjects. It is strongest when a chapter contains stable concepts that can be represented in several equivalent ways and checked against a source. It is weaker when the task depends on tacit professional judgement, contested interpretation or precise visual material the system cannot reliably parse.
STEM chapters often benefit because the learner can move between definitions, equations, examples and counterexamples. But STEM also punishes small errors. A wrong sign, unit or assumption can poison a whole derivation, so students should require step checks against the textbook or worked solutions.
Humanities chapters benefit from conceptual mapping and comparative explanation, but AI can flatten disagreement. A chapter on historiography, ethics or literary theory is not merely a set of propositions to simplify. The tension between interpretations may be the point.
Professional disciplines create a third category. Medicine, law, accounting and engineering textbooks often combine stable foundations with rules that depend on jurisdiction, guidelines or current standards. AI can be excellent for concept explanation while still being unsafe as a final authority.
| Chapter Type | AI Fit | What AI Does Well | Main Failure Mode | Human Check |
| Foundational maths/physics | High | Stepwise rephrasing, examples, practice | Confident algebra or assumption errors | Verify against worked solutions |
| Biology/anatomy | High–medium | Process maps, terminology, quizzes | Oversimplified causal stories | Check diagrams and source labels |
| Economics/social science | Medium–high | Model intuition, comparisons, scenarios | Treating assumptions as facts | Check model conditions |
| History/literature/philosophy | Medium | Concept maps, contrasting interpretations | Flattening scholarly disagreement | Return to primary/assigned texts |
| Law/medicine/accounting | Medium | Explain foundations and vocabulary | Outdated or jurisdiction-mismatched rules | Use authoritative current standards |
A Five-Pass Workflow for Difficult Chapters
Pass 1: Map Before You Ask for an Explanation
Skim the chapter headings, figures, learning objectives, summary and end-of-chapter questions first. Then ask the AI to reproduce the chapter’s conceptual map from the uploaded material, not from memory. The point is to give the learner a scaffold before detail.
Prompt pattern: “Using only this chapter, list the five ideas I must understand before the later sections make sense. For each one, cite the page or section and tell me what it depends on.”
Pass 2: Diagnose the Exact Point of Confusion
Do not ask “explain the chapter.” Ask where the chain breaks. Paste the paragraph, equation or diagram caption and require the AI to ask two or three diagnostic questions before explaining. This prevents a polished lecture that misses the actual gap.
Pass 3: Change Representation, Not Just Wording
If the first explanation fails, change the form: causal diagram, analogy, worked example, non-example, contrast table, mini-simulation or oral-style questioning. Rephrasing the same paragraph five times is less useful than seeing the concept from five structures.
Pass 4: Close the Source and Retrieve
Now remove the explanation. Ask for three closed-book questions: one recall, one application and one misconception trap. Answer before seeing feedback. This is the moment that separates an AI convenience from an AI tutor.
Pass 5: Reconcile With the Teacher’s Frame
Finally, compare the AI explanation with lecture notes, marking criteria, worked examples or the teacher’s terminology. Any mismatch should be resolved in favour of the course’s authoritative sources, or taken to the teacher as a question.
For students who need the source trail itself rather than only an explanation, the magazine’s Perplexity academic research guide explains why citation visibility helps with discovery but still requires opening and checking the underlying source.
The Accuracy Problem: Fluent Is Not the Same as Faithful
AI explanations fail in a particularly dangerous way: they can become clearer as they become less faithful. A model may replace an awkward but precise definition with a smoother approximation, invent a bridging fact, or merge two adjacent concepts because the result reads well.
The fix is to constrain the task. Upload the actual chapter. Tell the model to distinguish “from the chapter” from “added context.” Require page or section references. Ask it to flag uncertainty rather than fill gaps. For formulas, demand symbol definitions and unit checks. For quotations, return to the original page.
Source grounding also changes the student’s behaviour. When an answer points back to the chapter, the learner can verify. When the model simply speaks with confidence, the student is pushed toward trust rather than checking.
This is where educators’ warnings converge. HEPI policy manager Charlotte Armstrong said in 2026 that AI literacy “cannot be treated as optional.” Robin Gibson of Kortext argued for assistants “grounded in educational contexts” that enhance study skills. Neither claim means schools should standardise on a single product. Both point toward the same capability: students need to know how to interrogate an answer, not merely how to obtain one.
An effective AI chapter workflow therefore has an evidence rule: the AI may simplify, reorganise and question the source, but it may not silently substitute itself for the source.
That same evidence discipline appears in the site’s academic writing with AI guide which keeps the human responsible for source judgement and the final claim-to-evidence chain.
Which AI Tools Are Best for Textbook Explanation in 2026?
No single product has a universal advantage. The relevant features are source grounding, tutoring behaviour, file handling, multimodal explanation, practice generation and the ability to keep the learner doing the work.
ChatGPT Study mode is designed to ask questions, guide reasoning, work with uploaded files and create practice. OpenAI explicitly says it does not replace a teacher or course materials. Claude for Education and its learning mode use Socratic guidance rather than immediate answers, while Anthropic’s 2026 teacher offering emphasises standards-aligned planning and evidence-based curricula. Google’s Gemini ecosystem adds study notebooks and source-grounded notebook workflows, while Learn Your Way demonstrates Google’s research direction around transforming static material into personalised multimodal study experiences. Khanmigo remains the most education-specific of the group because it sits inside Khan Academy’s learning platform and is deliberately designed not to simply hand over answers.
Pricing is less informative than workflow fit, but students should know the commercial boundary. Vendor limits change frequently, so the table below uses only publicly documented consumer prices that could be verified at the time of research and marks unstable quotas as variable rather than inventing prompt counts.
| Tool | Verified Consumer Price | Textbook-Study Strength | Important Constraint |
| ChatGPT | Free available; Plus US$20/month | Study mode, file-based explanation, quizzes, multimodal help | Usage/model limits change; must verify source fidelity |
| Claude | Free; Pro US$20/month or US$200/year; Max US$100/$200 monthly tiers | Socratic learning mode, long-form reasoning, document work | Education pricing is institutional; consumer caps vary |
| Google Gemini / Google AI | Free access; Google AI Pro listed at US$19.99/month in current US pricing | Gemini study features, Notebook workflows, multimodal ecosystem | Plan features and regional offers vary |
| Khanmigo | Individual learner/parent plan documented at US$4/month in the US; teacher tools free in eligible locations | Purpose-built tutoring inside Khan Academy content | Learner access and geography are restricted; district terms differ |
A useful comparison with another source-heavy workflow is the magazine’s Claude study-guide tutorial which is especially relevant when students want long-document organisation without treating a generated summary as the end of studying.
Gemini users can apply the same human-led principle described in the site’s Gemini essay workflow: let the model transform and challenge material while the learner keeps the intellectual decisions.
When a Better Explanation Creates Worse Learning
The most under-discussed risk is not hallucination. It is cognitive outsourcing.
A chapter can feel dramatically easier when AI removes every obstacle: it summarises before the student reads, answers before the student attempts, resolves ambiguity before the student compares interpretations and produces practice with the answer key visible. The learner experiences speed, clarity and confidence. None of those guarantees memory.
The 2026 NBER experiment is instructive because structured AI students sometimes moved more slowly. That can be a feature. After an error, a useful tutor keeps the learner in the problem long enough to repair the model. A shortcut system simply delivers the correct answer and lets the learner move on.
HEPI’s survey captures the same split in student language. One respondent described AI as freeing time for “critical analysis and deeper understanding”; another said, “I’m not using my brain at all.” Those are not two opinions about the same workflow. They are two different workflows.
Students should watch for four warning signs: they cannot explain the concept without the chat transcript; they recognise an answer but cannot generate it; they keep requesting simpler explanations instead of attempting questions; or their AI-assisted homework is strong while closed-book performance stays flat.
The remedy is not to abandon AI. It is to impose friction deliberately: attempt first, request a hint second, ask for an explanation third, retrieve from memory fourth, and only then compare with the model.
The Strongest Model Is Teacher + AI + Retrieval Practice
The evidence points toward a division of labour rather than a winner.
Teachers should define the learning target, select authoritative material, anticipate common misconceptions and decide what counts as mastery. AI should provide immediate alternate explanations, low-stakes questioning, examples, language support and repeated practice. Students should perform the retrieval, application and comparison that converts explanation into knowledge.
This hybrid model also solves a scaling problem. A teacher cannot provide ten versions of an explanation to every student during a lesson. AI can. But AI cannot reliably decide which ten versions are instructionally worth giving without context. The teacher supplies that context through curriculum, assignments, examples, rubrics and intervention.
Sal Khan’s 2026 warning about technology receiving too much credit is useful here because it reverses the common framing. The question is not “Can AI replace this teacher explanation?” It is “What repetitive or adaptive layer can AI absorb so the teacher has more attention for diagnosis, feedback and relationships?”
For textbook chapters, that means the AI can become a private office-hours layer attached to the page. The student should still leave the session with a clean record of what came from the textbook, what was an AI-generated analogy, what remains uncertain and what needs a human answer.
For research-heavy chapters, the magazine’s Perplexity research workflow is a useful adjacent model: use AI to map and synthesise, then keep verification as a separate step rather than blending the two.
A Practical Decision Rule for Students
Use AI first when the obstacle is representation: the language is dense, the example does not connect, the prerequisite is unclear, you need a second modality, or you need immediate low-stakes practice.
Use a teacher first when the obstacle is judgement: the AI and textbook disagree, the course uses a specific convention, the topic is contested, feedback depends on a rubric, the stakes are high, or you cannot tell whether the problem is your understanding or the material itself.
Use both when the chapter matters enough to be assessed. Ask AI for a bounded explanation, then bring the unresolved point to class, office hours or a tutor with the exact passage and your attempted reasoning. That is more useful to a teacher than “I don’t understand chapter six,” and it turns AI into preparation for human help rather than a substitute for it.
The test after every AI-assisted chapter is simple: close the tool and explain the concept to an empty page. If you can reconstruct the logic, solve a new problem and identify where your confidence is low, the AI probably helped. If you mainly remember the AI’s wording, it probably did too much.
Our Editorial Verification Process
This explainer was built from a September 2026 review of current education research, official vendor documentation and live indexed pages from Perplexity AI Magazine. We prioritised randomised and quasi-experimental studies that measured achievement or retention, including the 2025 Harvard-led Scientific Reports RCT, Google Research’s Learn Your Way efficacy results, the 2026 Scientific Reports secondary-school mathematics study and the August 2026 NBER field experiment with more than 6,000 students. Adoption statistics were checked against HEPI’s March 2026 survey of 1,054 full-time UK undergraduates.
Product claims were checked against current official documentation for ChatGPT Study mode, Anthropic’s education products and consumer pricing, Google AI plans and 2026 student-learning announcements, and Khan Academy’s Khanmigo help centre. Where vendors publish variable or account-dependent usage limits, this article does not convert them into fixed prompt counts.
The requested sitemap.xml, sitemap_index.xml and post-sitemap.xml endpoints did not expose their XML contents in this browsing session. Internal links were therefore selected from verified, live, indexed Perplexity AI Magazine pages that are semantically closest to the topic, rather than being represented as a complete sitemap-derived inventory.
SERP research was used for competitive gap analysis only. The leading pages generally fell into three structures: AI textbook summariser product pages, step-by-step chapter-study workflows, and broad AI-versus-teacher comparisons. This article deliberately uses a different frame: it separates explanation quality from teaching quality, tests the comparison against learning outcomes, and introduces a task-routing model for deciding when AI, a teacher or both should handle a difficult chapter.
This article was researched and drafted with AI assistance and reviewed by the Sami Ullah Khan editorial desk at Perplexity AI Magazine. All data, citations, pricing figures, and named quotes have been independently verified against primary sources before publication.
Conclusion
AI tools can explain complex textbook chapters better than teachers in a narrow but meaningful sense: they can rephrase instantly, personalise examples, change modality, answer follow-up questions without time pressure and provide unlimited low-stakes practice. Well-designed studies now show that those advantages can translate into stronger learning and retention in specific settings.
But “better explanation” does not equal “better teacher.” The strongest results come from systems that borrow the disciplines of good teaching: scaffolding, diagnosis, questioning, feedback, mastery checks and source control. Teachers still hold the harder responsibilities of curricular judgement, motivation, standards, social context and interpreting ambiguous evidence of understanding.
The durable model for 2026 is therefore not replacement. It is orchestration. Textbooks remain the structured source, AI becomes the adaptive layer around difficult passages, teachers set and interpret the learning journey, and students do the retrieval and application that no explanation can outsource.
The open question is not whether AI can make a chapter feel clear. It is whether schools and students can design AI use so that clarity survives after the tool is closed.
FAQs
Can AI tools explain complex textbook chapters better than teachers?
Sometimes. AI can be better at instant rephrasing, personalised examples, repeated explanations and practice, and controlled studies show strong learning gains from carefully designed AI tutors. Teachers remain stronger at diagnosis, motivation, curricular judgement and recognising when a fluent answer hides a misconception. The best approach is usually to use AI as an adaptive layer around authoritative course material, not as a replacement for teaching.
Is AI better than a teacher for difficult maths or science chapters?
AI can be excellent for stepwise explanations, alternative examples and immediate practice, especially when it is grounded in the assigned material. It can also make subtle algebra, unit or assumption errors. For assessed work, verify against the textbook, worked solutions or teacher guidance and test yourself on a new problem without AI.
Can ChatGPT teach a whole textbook chapter?
ChatGPT can help map, explain and quiz a chapter, especially when the chapter is uploaded and Study mode is used. A whole-chapter prompt is usually weaker than a staged workflow: map the chapter, diagnose one confusing section, change representation, practise closed-book recall, then reconcile the explanation with course materials.
Does using AI to summarise a textbook improve learning?
A summary can improve orientation, but reading a summary is not the same as learning. Evidence from Google’s Learn Your Way research suggests that AI-transformed, interactive and multimodal material can improve retention compared with a standard digital reader. The active elements—questions, multiple representations and retrieval—matter more than compression alone.
What is the biggest risk of using AI for textbook study?
Cognitive outsourcing. The AI may remove the productive struggle that helps memory and transfer. If students ask for final answers before attempting the material, they can become fluent with the explanation without being able to reproduce the reasoning independently.
Which AI tool is best for textbook explanations?
It depends on the task. ChatGPT and Claude are strong general conversational tutors, Gemini and its notebook ecosystem are useful for multimodal and source-based study, and Khanmigo is purpose-built for education inside Khan Academy. Source grounding, tutoring behaviour and your course rules matter more than a universal winner.
How do I know whether an AI explanation is accurate?
Ground it in the uploaded chapter, require page or section references, separate source-derived statements from added context, verify formulas and terminology, and check any disputed point against the textbook or teacher. Fluency and confidence are not evidence of fidelity.
Should schools replace textbook teaching with AI tutors?
Current evidence supports carefully designed AI tutoring as a powerful supplement, not a blanket replacement. Outcomes depend on implementation, pedagogy, teacher involvement, learner behaviour and the subject. Schools should evaluate transfer to independent assessments rather than assuming that higher engagement inside an AI tool proves learning.
References
Armstrong, C., & Stephenson, R. (2026). Student Generative Artificial Intelligence Survey 2026. Higher Education Policy Institute. HEPI 2026 survey
Google Research. (2025). Learn Your Way: Reimagining textbooks with generative AI. Google Research: Learn Your Way
Ismail, S. M., Ibrahim, K. A. A.-A., & Hashemifardnia, A. (2026). The impact of artificial intelligence-based tutoring systems on student engagement and long-term retention in math education. Scientific Reports. Scientific Reports 2026 AI tutoring study
Kestin, G., Miller, K., Klales, A., Milbourne, T., & Ponti, G. (2025). AI tutoring outperforms in-class active learning: An RCT introducing a novel research-based design in an authentic educational setting. Scientific Reports, 15, 17458. Scientific Reports 2025 RCT
Oreopoulos, P., Liut, M., Sungu, A., & Low, N. (2026). Making AI Tutoring Productive: Evidence from a Mastery-Based Math Practice Experiment. NBER Working Paper 35621. NBER Working Paper 35621
OpenAI. (2026). Using study mode in ChatGPT. OpenAI Study mode documentation
Anthropic. (2026). Introducing Claude for Teachers. Anthropic Claude for Teachers
Khan, S. (2026). Credit the Educators, Not Just the Technology. Khan Academy. Sal Khan on educators and technology
Khan Academy. (2026). Kristen’s Corner Winter 2026. Khan Academy learning measurement interview
