AIEd Knowledge BaseAIEd KB
On this page

Concept

Teaching

created 2026-08-09 · updated 2026-09-25

Teaching — how AI reshapes the work, identity, and agency of educators. With 50+ articles examining this dimension, the knowledge base documents a fundamental transformation: from sole knowledge authority to orchestrator of human-AI learning environments. This page goes beyond describing that shift — it details what teachers actually do differently, how they can adapt their practice, and how they connect to Learning Design, AI Literacy, and Academic Integrity.

Questions to Consider

  • The page describes the teacher's shift 'from sole knowledge authority to orchestrator of human-AI learning environments.' What do you gain and what do you lose as an educator when you stop being the main source of content?
  • It argues AI reallocates a teacher's scarcest resource — attention — from producing materials to interpreting learners. Does that reframing ring true, and what would you actually do with the reclaimed time?
  • Research cited on the page shows teachers as active AI designers — writing the prompts and scaffolds that shape how AI behaves for their students — rather than passive consumers. What would it take for you (or teachers you know) to feel like a designer of AI rather than a user of it?
  • At the far end of the 'teacher-AI teaming' spectrum, the teacher orchestrates a team of human learners, AI tutors, and curriculum resources. Where does human judgment remain irreplaceable in that team, and what might quietly erode if the orchestration is left mostly to AI?
  • If the teacher's attention is reallocated from producing materials to interpreting learners, what new competencies and risks does that introduce — and who supports the teacher in developing them?

Introduction

Teaching here names the professional role as AI reshapes it: less content delivery, more orchestration of human and machine participants, interpretation of what learners actually understand, and design of the conditions under which AI use is legitimate. The pages collected under this concept document teacher–AI teaming and workflow change, the competencies the role now demands, and the tension between efficiency gains and the attention that individual support requires. It sits alongside Educational Development, AI Literacy and Assessment as one of the roles that determines whether AI integration changes practice or merely decorates it. The learning sciences are the research field whose evidence reshapes this position: they describe and test how learning happens and which designs change it, while this page covers the classroom role that has to act on that evidence in the moment.

How AI transforms teaching

  • From instructor to orchestrator: Five levels of teacher-AI teaming and agency orchestration research map the spectrum from AI as tool to AI as teaching partner. At the far end, the teacher orchestrates a team that includes human learners, AI tutors, and curriculum resources rather than delivering all content themselves.

  • Workflow transformation: How AI Is Changing Teaching Workflows documents how AI shifts teacher time from content delivery to higher-value activities like individual support, Feedback, and Curriculum Design. The teacher's scarcest resource — attention — is reallocated from producing materials to interpreting learners. A systematic review of language educators (Li et al. 2026) corroborates this: educators value GenAI most for preparatory work (lesson planning, materials creation, writing support) while hesitating on live classroom use, and their adoption is shaped by professional-identity, pedagogical, technical, institutional, and academic-integrity factors — with competency gaps mapping to episteme (understanding AI's capabilities/limits), techne (Prompt Engineering, AI-enhanced task/assessment design, detecting AI-generated text), and phronesis (ethical judgment, bias/privacy handling, context-sensitive judgment). documents how AI shifts teacher time from content delivery to higher-value activities like individual support, Feedback, and Curriculum Design. The teacher's scarcest resource — attention — is reallocated from producing materials to interpreting learners.

  • Competency demands: Teacher AI competency frameworks define what educators need to know, from basic tool fluency to pedagogically grounded orchestration. Adoption studies identify the real barriers: confidence, institutional support, and workload concerns. Bai & Hsieh (2026) add an empirical weight to this in an SEM of 898 Chinese university teachers: GenAI-supported teaching innovation depends less on isolated AI technological knowledge and more on pedagogically and disciplinarily embedded AI competence (AI Literacy and professional identity), both of which partially mediated the competence→innovation relationship — a signal to build faculty development around embedded, identity-linked AI competence rather than tool-only training.

  • Readiness as a design capability, not an attitude: Charles (2026) argues that teacher readiness for AI-rich interactive learning cannot be read off adoption, confidence, or digital competence, because none of these predicts the quality of the designs teachers actually produce. Readiness-by-design is enactment-oriented: teachers mobilize AI-related pedagogical knowledge, pedagogical beliefs, Self-Efficacy, and ethical orientation through design mediation (tool selection, task and prompt design, scaffolding, transparency, verification, assessment redesign, accessibility planning, and human oversight) inside an institutional and policy ecology that enables and moderates the translation. Two claims carry practical weight. Self-efficacy is a mobilization resource rather than a quality indicator, so confidence without AI-related pedagogical knowledge or ethical orientation can accelerate poor design. And policy clarity and material support are separate moderators, so a well-resourced school can still leave teachers guessing what is permitted. Charles also declines to equate non-use with unreadiness: a reasoned decision to withhold AI can itself demonstrate readiness.

  • Co-design and agency: Teacher-authored prompts and vibe coding for teachers show educators as active AI designers, not passive consumers — writing the prompts and scaffolds that shape how AI behaves for their students. In children's STEAM arts lesson planning, Luo and Tahir (2025) experimentally confirmed that how the teacher delegates to ChatGPT matters: filling content gaps in a self-outlined lesson (the method favored by 60% and recommended) preserved teacher design autonomy while still raising expert-rated plan quality (median 20.5 vs. 17.6, p = .002, large effect), outperforming having AI generate whole plans or merely checking a finished one. AI served chiefly as an inspiration and gap-finding collaborator rather than a source of fully original innovation — reinforcing that the teacher remains the design agent and judge of output, including catching child-safety and cultural-bias flaws the tool missed. A systematic review of teacher–AI co-design of learning tasks (Wang, Liu & Islam 2026) finds this co-designer stance remains uneven — the dominant mode is still AI as assistant/content generator — and Talebzadeh (2026) shows the teacher's agency as "bilingual learning designer" is what turns AI-designed group activities into differentiated, ZPD-aligned instruction.

  • Preservice preparation: TPACK-based training and Educational Development programs prepare future teachers for AI-augmented classrooms before they enter them. Simulated-student role-play extends this into hands-on practice: a custom ChatGPT bot (Student GPT) playing a mathematically misconception-ridden middle schooler lets preservice teachers rehearse diagnosing and guiding student reasoning in a low-risk setting, preparing them for a central teaching task — reading and remediating student thinking.

  • A teacher-facing frontier the evidence base has barely entered: Štefánik et al. (2026) assemble Edustories, 1,492 teacher-written case studies of real elementary and high-school classrooms — challenging student behavior, the intervention the teacher attempted, and what followed — precisely because most AI-in-education research has targeted individualized student assistance while most teaching happens in collective classrooms. Benchmarking four language-model families on predicting whether an intervention succeeded, the strongest models reached 58% accuracy against 64% for human experts; the authors read the gap in both directions, as a limit on current teacher-facing assistance and as evidence of emerging potential. What the page gains from it is a measured ceiling rather than a capability claim: advice to teachers needs to be better than expert judgment before it can be trusted, and today the ordering is the other way. A parallel gap appears in Yalçın et al.'s (2026) PRISMA review of 46 empirical studies of GenAI in higher-education programming: instructor roles and institutional practices were among the least studied areas of the field, and 26 of the 46 studies gave no codable description of their instructional approach, so the design decisions that produced reported outcomes are often invisible even to other instructors.

  • Where AI actually enters the work, by task: Holster (2026) separates adoption from allocation using TALIS 2024 data from 56,669 teachers, with an allocation sample of 24,058 AI users across 46 education systems. A task rated one point above a teacher's own mean demand was more likely to receive AI help (OR = 1.163), but the direction differs by task: planning rises with demand (OR = 1.084), assessment and marking falls (OR = 0.921), and special-education support and adaptation rises steeply (OR = 1.442, a predicted spread from 34.0% to 50.3% across the demand range). The competency and policy reading is that teachers already keep high-stakes marking human while reaching for AI on individualized adaptation work, so guidance that treats teacher AI use as a single behavior will miss where support is actually needed.

What the changing role actually looks like: concrete examples

The abstraction "orchestrator" is easier to grasp through concrete, day-to-day shifts in instructor work:

  • From writing every handout to curating AI-generated drafts. An instructor who once spent an evening building a differentiated worksheet now prompts an AI to produce three versions at different difficulty levels, then spends the same evening reviewing and adapting them — a shift from authoring to editorial judgment. Curriculum-as-Code workflows make this pipeline explicit, and Kibar & Ilgaz's systematic review shows AI acting as a "co-worker" that drafts content the designer then refines. Expert validation of AI-generated science lesson plans makes explicit what that editorial judgment must weigh: Karaismailoglu, Surmeli and Yildirim (2026) found eleven Science Education specialists rated ChatGPT-4 and an education-focused tool's sixth-grade plans as usable drafts — 7 of 11 judging them "applicable by correction," only 3 directly "Applicable" — and preferences diverged from raw scores (7 preferred the higher-scoring education-focused tool, 4 the general-purpose plan) because judgment weighed affective and contextual dimensions beyond structural fidelity. Teachers remain the arbiters of whether and how an AI draft becomes a teachable, contextually grounded lesson.
  • From lecturing to real-time coaching. Rather than delivering the same explanation to the whole class, teachers increasingly route routine questions to an AI tutor and focus face-to-face time on the students who need human judgment — workflow studies document exactly this reallocation of attention.
  • From grading to designing assessments that AI can't game. Instructors stop relying on recall-based tasks that LLMs trivially solve and instead design authentic assessments, assessment scales, and formative tasks that preserve Learning Gains.
  • From teaching content to teaching use. The teacher's job increasingly includes modeling how to prompt, evaluate, and responsibly use AI — an AI Literacy curriculum woven into every course, not a separate subject.
  • From reading dashboards to acting on them — contextually. Mejia-Domenzain et al. (2026) show that how teachers actually use analytics dashboards diverges by context: flipped-classroom (university) teachers followed a sequential exploration and favored course-level adaptation and showing dashboards in class, whereas vocational teachers revisited summary pages and used the tool mainly for individual coaching sessions. The actions teachers proposed were shaped by the content represented and their teaching level rather than the plot type — university teachers favored weekly tests and course adaptation, vocational teachers direct, individualized coaching. This points to context-aware dashboard design and differentiated teacher support needs rather than a one-size-fits-all analytics interface.
  • From gatekeeper to principled facilitator of collaboration feedback. The Community Builder (CoBi) classroom pilots show the teacher role decisively shapes implementation integrity: teachers who used the AI's noticings and visualizations to spark Metacognition and critical reflection about collaboration achieved high-integrity use, while those who let the system drift into performance-monitoring — or veer off into general AI discussions — did not. Teachers also worried about being put "on the spot" by real-time feedback and preferred pre/post-action review over live display, underscoring that orchestrating classroom-wide collaboration AI demands substantial professional learning, not just tool fluency.

The orchestration metaphor

The dominant metaphor in the knowledge base is orchestration: teachers coordinate human learners, AI tutors, and curriculum resources. This contrasts with replacement narratives — AI augments rather than substitutes for human teaching. Empirical evidence supports this stance: a PRISMA-guided systematic review of 42 studies (2023–2025) found LLMs match human raters on short, well-structured tasks but that performance declines on longer, multilingual, and nuanced work — concluding LLMs cannot fully replace teachers and that hybrid, human-in-the-loop assessment systems achieve the highest grading effectiveness (Can ChatGPT Replace the Teacher in Assessment? A Review of Research on the Use of Large Language Models in Grading and Providing Feedback). The orchestration lens reframes the instructor's core skill as judgment: deciding when a human, an AI, or a designed learning activity is the right instrument for a given learner and moment. A 2026 PRISMA review of 29 studies of Teacher intervention in K-12 AI-based instruction: a systematic review of processes, strategies, and effects sharpens the metaphor into a four-phase cycle — monitoring, judgment, intervention, orchestration — and shows why no phase can be assumed. Teachers preferred shared control, accepting, modifying, rejecting or overriding AI suggestions; some deferred intervention deliberately so students could struggle productively first; and dashboards that expanded awareness could also overload attention or exceed what one teacher could physically act on. Its three strategies — pedagogical translation of AI output, design of learning support, and reconstruction of interaction structures — describe the work as recontextualization rather than approval, which places the teacher closer to mediator than to the human-in-the-loop reviewer role.

Evolving and critical teacher roles

Recent work expands the orchestration metaphor into richer role conceptualizations:

  • Co-orchestrator across activity transitions. Yang et al. (2026) use participatory speed dating with 17 teachers and 13 students to map how control should be distributed across the stages of a classroom activity — the before, during, and after transitions between individual and collaborative work — in a co-orchestration tool supporting real-time dynamic pairing. The design principle that emerges is that control is not a fixed point: teachers and students want different amounts of agency at each stage, and the tool should let the teacher adjust grouping, activity transitions, and intervention timing in real time while retaining a teacher override. This positions the teacher as a co-orchestrator of the whole live learning environment — a role more specific than the general orchestration metaphor, focused on the granular, moment-to-moment decisions of who works with whom, when, and with what support — and it connects individual-tutoring research to classroom-level design.
  • The "cognitive choreographer." Posthumanist frameworks recast the teacher as a cognitive choreographer who orchestrates cognition distributed across biological and artificial systems, moving beyond instrumentalist models like Technological Pedagogical Content Knowledge (TPACK) and SAM.(Pedagogical Symbiosis: conceptualizing the Post-Human Learner in the age of cognitive AI)
  • Facilitator, co-investigator, ethical supervisor. In science learning, teachers' roles shift from knowledge transmitters to facilitators and co-investigators, and gain new responsibilities as ethical supervisors of students' responsible AI use.(Artificial Intelligence in Science Learning within the Framework of Situated Learning Theory: A Qualitative Investigation of Teachers' Perspectives)
  • Mediator of learning principles. Educators operationalize age-old learning principles (experiential, situated, and distributed cognition) through AI, treating AI as a tool that enhances rather than replaces the educator's guiding role.(Empowering Educators: Operationalizing Age-Old Learning Principles Using AI)
  • Critical mediator who turns AI output into an object of critique. Canonigo (2026) followed ten secondary mathematics teachers through an eight-week ChatGPT-4 intervention and identified five mediation practices: AI-generated problem posing, hint scaffolds designed to fade, critical comparison of AI output against students' own work, differentiated prompting (a visual analogy for a struggling student, a cubic extension for a peer), and AI as a reflective dialogue partner. Where teachers enacted these, output became something to argue with rather than an answer key, and students moved toward conjecture and metacognitive questioning; where mediation was absent, authority migrated toward the algorithm, with students consulting the AI before their teacher and returning only for validation, prompting one teacher to describe the move from "oracle" to "editor." The study's equity finding is the sharp one: across 300 model responses, the free-tier model was inaccurate in 32.7% of instances against 12% for the premium model (χ²(1) = 17.5, p < .001), so teachers in the under-resourced school called it "borderline useless for mathematics." Tool quality, not just tool access, becomes a pedagogical variable, and one teacher's school acceptable-use policy was a decade old and silent on AI, leaving the governance gap to the classroom.
  • Unsettled and vulnerable professional identity. Farazouli et al. (2026) document the emotional side of role reconfiguration: 24 Swedish university teachers experienced GAI's emergence as alarming and overwhelming, reporting a "state of vulnerability" (low confidence, insecurity, fear of "not being ahead of students") and feeling "stuck" between utopian and dystopian discourses. GAI prompted them to re-evaluate their priorities — cultivating Critical Thinking, Evaluative Judgment, and ethical GAI use — and to question their own and the university's future role in a landscape that felt "out of control." This frames the teacher-role shift as identity-level and emotionally charged, not merely a skills or workflow change.
  • Academic developers as digital mediators, including as a brake. Sithole (2026) interviews twelve academic developers and learning designers at two South African Historically Disadvantaged Institutions and finds the role shifting from facilitating reflective teaching toward technological intermediary and problem-solver, with mediation spanning pedagogy, ethics and institutional politics. The distinctive move is treating slowing down as part of the job: one participant describes translating between "what management wants, what the technology can do, and what lecturers are actually worried about," and naming plagiarism, student dependency and the outsourcing of thinking before enthusiasm sets the pace — "It is not just technical support; it is also negotiation." The same study documents the affective cost ("I feel like an impostor. I am learning AI as I go, but the institution expects expertise") and the professional-learning response: peer spaces that test and deliberately break tools, aiming at judgment rather than skill, and an explicit refusal of the adoption-or-resistance binary — "I am not anti-AI; I am pro-context."
  • Principled selectivity instead of adoption or resistance. Adiozaman and Segar (2026) interviewed two academics with more than ten years' experience three times across a single semester and found identity work organized around three interrelated tensions — pedagogy versus platform, educator versus facilitator, and care versus compliance — which resolved not into a settled position but into principled selectivity: context-sensitive decisions guided by pedagogical values, ethical commitment and professional judgment. They describe a trajectory from implicit orientation, through mid-semester strategies such as asking students to explain their thinking and redesigning tasks and assessment, to stances that stayed deliberately open rather than fixed, with refusal of a particular use treated as a legitimate exercise of judgment rather than a failure to adopt. This supplies the constructive counterpart to the vulnerability documented above: what the role shift demands is judgment, so institutional responses built around tool training address the wrong problem.
  • Semi-autonomy as the preferred operating point. Dai et al. (2026) surveyed 287 GenAI-experienced teachers in 27 countries and regions and found them placing the tool at the semi-autonomous levels of an automation scale — 130 chose Level 2 ("teacher assistance"), 94 Level 3 ("partial automation"), and exactly one chose Level 6 — describing it as "auxiliary", "scaffold", "complement" or "partner". Perceived usefulness dominated their adoption intention (β = .832, p < .001), while perceived artificial autonomy influenced intention only indirectly through usefulness, and risk aversion depressed intention without denting usefulness because their worries attached to students' use — cheating and integrity, weakened foundational knowledge and higher-order thinking, hallucinations, reduced human interaction, and ethical, legal and Equity risks. The authors' "frenemy" label captures the stance precisely: GenAI valued as a support tool but distrusted as an autonomous agent. The corollary for the teaching role is that teachers want to remain the deciding agent and to settle use case by case — discipline, student needs, task type, timing — so orchestration tools should be built around human oversight and case-level judgment rather than autonomy promises.
  • Authority stays with the teacher; capability expands only inside it. Reichert, Briceno, Tabarsi & Barnes (2026) gave six secondary teachers across grades 6-12 (social studies, mathematics, animal science, science, and computer science and robotics) the task of paper-prototyping an LLM chatbot for their own classroom, and found that every prototype encoded the same stance: the system was a bounded expert, specialized but confined to a defined domain and supervised by the teacher. Teachers drew two boundaries. Authority boundaries followed from professional and legal responsibility — control over what students learn and how they are kept safe could not be delegated, so oversight (complete conversation logs, real-time alerts for inappropriate queries, and manual override) was treated as a duty of the role rather than a check on the tool. Expertise boundaries followed from what the model cannot know: individual students' histories, classroom dynamics, and institutional norms. Delegation was then allocated selectively across instruction — teachers welcomed AI to present content, supply practice problems, scaffold, and give formative Feedback, but kept informing students of objectives and summative Assessment themselves. This supplies the semi-autonomy preference documented above with a concrete architecture: the teacher's authority is the fixed element and AI capability grows only within it.
  • Teachers as value-laden graders. Luo & Dawson (2026) show that grading GenAI-assisted work is not a neutral, criteria-based act but a value judgment shaped by teachers' conjecture about who the student is (honesty, diligence), what they are capable of (independence, GenAI skill, disciplinary mastery), how they relate to others (trust), and whether the decision leads to good outcomes (fairness, beneficence). Teachers often lack awareness of the value orientations underlying their grading, and these vary by discipline (humanities more critical of GenAI use; hard/applied sciences fewer grading challenges) and epistemological stance (absolutist/multiplist/evaluativist). This positions the teacher as an evaluative professional whose judgment — not just tool use — must be supported and made transparent.
  • Teacher as the arbiter of pedagogically meaningful explanations. Because teachers' trust in AI recommendations rises when explanations are framed in their curricular/pedagogical language rather than in raw model internals, effective tools must "speak" the teacher's domain. In a within-subject experiment, Feldman-Maggor et al. (2025) found domain-driven explanations of an AI grouping tool were trusted and accepted far more than data-driven feature-importance ones, positioning the teacher's pedagogical vocabulary as the interface through which AI earns trust and use — and their judgment of whether an explanation is pedagogically sound as a deciding factor in adoption.
  • Facilitator whose function can be partly delegated. Kuhail et al. (2026) had 32 STEM students debate under a human moderator, an AI chatbot (GPT-4, prompted with the behaviors expected of faculty: neutrality, equal speaking time, short directions, on-topic enforcement, follow-up questions, de-escalation), or each in turn, and found no significant difference on effectiveness, enjoyment, satisfaction, utility, or intention to reuse (t = 0.23 to 1.30, p = 0.198 to 0.818). The authors read the null result as normalization of human-AI collaboration in rule-bound discussion, not as equivalence. The route to reuse did differ: under human moderation satisfaction drove intention to use, whereas under AI moderation perceived utility did (0.806 against 0.495), so an AI-facilitated activity survives on demonstrated usefulness rather than on how enjoyable it feels. The practical reading is AI moderation as a scaling aid for large or lightly staffed courses under human oversight, with equity unverified for sarcasm, indirect language, and minority communication styles.
  • Setter of purposes, not just competencies. Fagerlund et al. (2026) interviewed 13 Finnish teachers from preschool to grade 9 about why students should learn about and with AI, reading their accounts through Biesta's qualification, socialization, and subjectification. Qualification was the clearest and most concrete purpose, covering AI as both a target of learning and a tool for it; socialization appeared as cultivating favorable stances toward AI and preparing students as AI-savvy future workers; subjectification, self-determined and personally meaningful engagement, was endorsed as important but had no concrete instructional strategies behind it, resting on "discussing and contemplating." The authors propose informed AI agency, subjectification at the center with competencies as its foundation, which gives the teaching role a purpose-setting duty: make the "why" explicit so skills acquire context rather than standing as ends in themselves, and complement technical frameworks such as Technological Pedagogical Content Knowledge (TPACK) with sustained attention to educational purpose.

These roles connect teacher work to Distributed Cognition, Situated Learning, Embodied Learning, and Critical Pedagogy, and reframe the teacher as a designer and ethical guide of AI-mediated learning rather than merely a user.

  • AI-supported decisions cluster where behavioral data is available. Köroğlu et al. (2026) reviewed 27 empirical studies (2016–2025) and identified eight lecturer decision types, finding AI support concentrated on instructional, Feedback and Assessment decisions while curriculum, learning-environment, emotional, ethical and administrative decisions were rarely supported. Learning Analytics Dashboards were the most common system, processing text and log data into behavioral indicators of performance and engagement; Multimodal AI and interaction-based data, agentic systems and cognitive, metacognitive, motivational and affective outcomes were all comparatively rare in the reviewed corpus. The practical reading for instructors is that the decision space an AI tool opens is bounded by the data it displays, so tool selection is also a choice about which teaching decisions are being resourced.

How instructors should adapt their teaching practices to AI

The knowledge base's evidence converges on a set of concrete adaptations:

Design the AI's pedagogical wrapper, not just the tool. The same AI yields large Learning Gains or net harm depending on how the activity is designed around it. Kibar & Ilgaz and the Learning Gains evidence are consistent: the instructor's job is to design the learning experience, treating AI as a component within a structured activity rather than the answer engine. Scaffold a student attempt first, then let AI coach — this is the difference between assessment and performance inflation.

Require a Human-in-the-Loop checkpoint. Rather than accepting AI output at face value, teach students to evaluate, correct, and take ownership of AI-assisted work. Fair-use AI literacy frames this as balancing AI's productivity against genuine human flourishing and authorship. Design assignments so AI helps with drafting but the learner remains the agent of evaluation and revision.

Curate AI feedback drafts; do not relay them. Chen et al. (2026) gave 64 Chinese pre-service teachers ChatGPT-assisted feedback on argumentative writing and found that treating the output as draft material rather than finished comment moved critical thinking where teacher-only feedback did not (post-test Cohen's d of 3.74 against 0.25 at pretest, p < .001). What the teachers did with the draft is the transferable part: they copied appropriate content at a level similar to their own (28%), expanded it with deeper input (16%), supplied it (16%), revised it (14%), or discarded it entirely (14%), with a further 9% needing correction. The assisted feedback leaned toward modeling (demonstration, 33.1% against 10.9%) and probing (question-closely, 21.5% against 6.0%), and students revised it toward analysis, evaluation, and creation rather than recognition, but six of eight interviewees still reported imprecision and unprofessionalism. The teacher's verification step is what makes the hybrid safe.

Adapt assessment to the age of AI. Move away from tasks LLMs can complete verbatim toward authentic, process-based, and in-person assessments. Assessment scales give instructors a rubric for deciding how much AI assistance is legitimate at each stage, and meta-analytic evidence shows AI can support higher-order thinking when the assessment design demands it.

Protect the conditions for durable learning. Because AI answers make learning look easy, instructors must design to counter the performance-learning gap. Lodge & Loble warn that effortless success with AI can mask the absence of learning; instructors should build in productive struggle, require unassisted demonstration of mastery, and treat AI-supported answers as a starting point, not the finished outcome.

Plan for the delivery medium. Adaptation is not medium-neutral: online teaching with AI multiplies both the opportunities (scalable personalization, always-on support) and the risks (integrity, offloading). Design the AI wrapper as deliberately in online as in face-to-face contexts.(Can AI deliver appropriate support for diverse student profiles? A large-scale evaluation)

Incorporating AI literacy into teaching

AI literacy is not a separate module — it is woven into how instructors design every course. Effective approaches include:

Ensuring academic integrity in the age of AI

Academic integrity with AI is a design problem, not a policing problem. Instructors adapt by:

  • Reframing integrity around process and authorship. Instead of detection, emphasize Academic Integrity as transparent, documented use. Agentic AI and ghost-student research shows the integrity risk shifts when students deploy AI agents on their behalf, so instructors must define what authorship means when the "ghost" is an AI.
  • Using disclosure and framing. How AI use is framed for students — whether as a crutch or a legitimate tool — shapes whether they disclose it. Transparency norms (e.g., AI-use disclosure) reduce the incentive to hide AI use.
  • Designing out the incentive to cheat. Authentic, process-based, and in-person assessments (oral exams, portfolios, observed Problem Solving) make outsourcing less attractive than AI-detection tools do. Assessment scales and Authentic Assessment are the constructive alternative to detection arms races.
  • Task-level AI Regulation in Education is how instructors actually govern AI. Chirikov's (2026) study of 31,000+ course syllabi shows instructors increasingly acting as task-level regulators rather than applying blanket rules: they restrict AI for drafting/revising (79% of courses) and reasoning/problem-solving (65%), permit it for editing/proofreading (83%) and study support (80%), and leave ideation/planning most contested (46% permit / 54% restrict). This differentiation — built on which tasks AI displaces versus augments — is a concrete, instructor-driven alternative to adoption-or-ban policy and gives the teacher-role a central place in policy formation.

Connecting teaching to learning design

Teaching and Learning Design are two sides of the same coin — the instructor's daily judgments are the live execution of the designed learning experience. AI tightens this connection:

  • Teachers as learning designers. LearnAI and teacher-authored prompt design show instructors functioning as designers: specifying the activity, the AI's role, and the scaffolds that structure learning. This is Learning Design in action at the point of use.
  • Learning-design principles govern AI pedagogy. The same Learning Design principles — clear objectives, aligned assessment, Scaffolding, and Feedback — determine whether AI helps or harms. ISD-Agent-Bench empirically validates that grounding AI design in formal instructional-design models beats theory-free prompting.
  • Design for the teacher's orchestration. Effective AI learning environments are designed with the teacher in mind — the tools human-centered AI provide should reduce teacher workload and augment judgment, not add another opaque black box. When instructors co-design AI learning activities (activity-theory perspectives), adoption and quality both improve.

Relationship to learner identity

Teacher role and Learner Identity are reciprocal faces of the same human process, and AI reshapes both.

  • Teacher identity is a professional identity; learner identity is a learning identity. The teacher-role page documents how AI reshapes the work, identity, and agency of educators — their evolving professional self-understanding (see Laidlaw's framing of GenAI as an identity crisis). Learner identity is the parallel construct for students: who they are and are becoming as learners, in disciplinary, professional, creative, and academic terms.
  • They are causally coupled. Teachers who experience identity disruption (uncertainty about their professional purpose amid GenAI) are less able to support their students' identity development — a teacher who doubts their role struggles to validate students' emerging sense of self in the same domain. Conversely, teachers who sustain a confident professional identity are better positioned to scaffold students' belonging and authorship.
  • Distinct failure modes. Teacher identity is threatened by role obsolescence and purpose (the "what's the point of teaching?" question). Learner identity is threatened by authorship loss and competence (the "is this really mine / am I good enough?" question). Both are identity-level (not just skills-level) responses to AI.
  • Both are professional-development and pedagogical concerns. Supporting teacher identity belongs to Educational Development and Teacher AI Competency; supporting learner identity belongs to Student Experience, Authentic Assessment, and Learner Agency. A well-designed AI-integrated system attends to both — because the teacher's identity is the condition under which learners' identities form.

Teacher Co-Design of Early AI Literacy

  • Teacher co-design in early AI literacy. Lee (2026) shows that two pre-K and two kindergarten teachers who co-designed the Play With AI (PL-AI) curriculum experienced substantial growth in confidence and pedagogical agency, with co-design fostering curriculum ownership, reflective practice, and meaningful adaptation. This positions teachers as central co-designers — not just implementers — of developmentally appropriate AI literacy curricula, a model with implications for teacher preparation in early childhood AI education.

Teachers Co-Designing AI Learning Resources

  • The teacher's role extends to co-designing AI learning resources: teacher-AI co-designed simulations for drone STEM instruction kept GenAI output pedagogically valid and contextually relevant, and teachers are the intended beneficiaries of the Teachers' AI Literacy Scale. Effective GenAI integration increasingly depends on teacher involvement in design.

Connected Concepts

Connected Articles

Connected FAQs

Metadata

LevelK 12, Higher Ed
Intended audienceInstructors, Faculty Developers, Learners
Learning and instructionScaffolding
Technologies and techniquesIntelligent Tutoring
Foundations of AI in educationAI Literacy, Educational Development, Learning Design, Teacher AI Competency
SourceHistoryEditChat

Embed this page

Copy the code below to embed a chromeless version of this page in a learning management system or other website. The embedded view hides the site header, navigation, and footer.