Teacher AI Competency

Teacher AI competency — the knowledge, skills, and dispositions teachers need to effectively, ethically, and equitably integrate AI into teaching and learning. It extends beyond technical tool use to include pedagogical integration, assessment literacy, ethical judgment, and the confidence to use AI well. Teacher AI competency is the teacher-side counterpart to AI Literacy, and is developed through professional development. It is central to how the teacher's role is transforming in AI-augmented classrooms.

Questions to Consider

• The page argues the teacher is 'the decisive factor' in whether AI improves learning — that tools only help when teachers can plan for them, scaffold use, and evaluate outputs. Does that match your experience, or do you think the tool itself matters more than the teacher?
• Teacher AI competency spans technical proficiency, pedagogical integration, assessment literacy, and ethical judgment. Which of these do you think teachers most lack, and which is hardest to train?
• The research documents a gap between teachers' self-perceived and actual AI skill. Why do you think people overestimate their readiness, and what would it take to close that gap honestly?
• An intensive professional-development program produced large gains in AI pedagogical skill in the research cited, suggesting technical-pedagogical skill is trainable. If that's true, why do so many teachers still seem unprepared — what's standing in the way?
• If a teacher can 'use AI well,' what does 'well' mean to you — and how would you know a teacher has achieved it rather than just adopted the tool?

Introduction

Teacher AI competency matters because the teacher is the decisive factor in whether AI improves learning. Research consistently shows that AI tools only translate into better outcomes when teachers can plan for them, scaffold student use, evaluate outputs, and integrate them into coherent instruction. The knowledge base's literature examines the dimensions of this competency, the gaps between self-perception and actual skill, and the professional development that builds it.

Nunez-Canal et al. (2026) connect faculty AI competency to student outcomes directly, using human capital and stakeholder theories on a survey of business and economics faculty to argue that universities' investment in faculty competency is not discretionary.

• Prompting training saves time but does not improve materials. In a preregistered RCT with 204 teachers, Appel, Fervers and Piepenburg (2026) found ChatGPT improved the quality of lesson-preparation materials and cut preparation time by 34%, while prior AI training added a further 21% time reduction without improving rated quality (Appel et al., 2026). Trained teachers wrote longer prompts and iterated less; both routes reached equal quality.

Core competency dimensions

The knowledge base's research converges on several interconnected dimensions:

• Technical proficiency: crafting effective prompts for educational objectives, evaluating AI tools for pedagogical fit and safety, and troubleshooting failures in real time. An intensive GenAI PD program documented significant gains across all five AI-PCK components (overall d = 2.36), showing technical-pedagogical skill is trainable.

• Specifying agent workflows: when teachers configure a chatbot in natural language, the dominant failure is lost intent rather than wrong facts — bots skipped required workflow steps in 53.8% of end-user conversations and omitted expectation setup in 33.4% (Liu et al. (2026)), so writing ordered, explicit steps is a trainable competency dimension of its own.

• Pedagogical knowledge is the decisive layer. A cross-level study of secondary AI education (46 teachers, 2,832 students) found technical AI knowledge alone was insufficient — and could even slightly reduce students' perceptions of AI for social good — whereas pedagogical AI knowledge drove students' perceptions and their intention to learn AI. Competency frameworks should therefore weight pedagogical AI knowledge as the pivotal dimension, not a soft add-on.

• Pedagogical integration: mapping AI use to learning objectives, designing Scaffolding that supports student Metacognition and self-AI Regulation in Education, and integrating AI into instructional design. AI-TPACK research models how teachers combine technological, pedagogical, and content knowledge through multi-agent workflows, while a five-level teacher-AI teaming framework (transactional → synergistic) captures how GenAI may replace, complement, or augment teacher competence.

• Research competence: using AI for classroom data analysis is a trainable dimension of teacher expertise; a six-week AI-supported program with 105 chemistry teachers produced pre-post gains on all measured dimensions (Cohen dz 0.47 to 0.63, p < 0.001) (Ussenbay et al. (2026)). It also shifted teacher perceptions of AI from retrieval and writing toward research planning and data-informed decision-making.

• Assessment literacy: evaluating AI-generated content and student AI outputs, and understanding how validity shifts when students use AI. This connects to Automated Assessment, AI Detection, and the broader Assessment redesign agenda. For recommendation systems, this extends to judging whether an AI's explanations are genuinely understandable and pedagogically meaningful: Feldman-Maggor et al. (2025) found teachers trusted AI grouping recommendations more when explanations were framed in domain (curricular) language than when they exposed raw model features — a skill for demanding and appraising explanation quality rather than accepting opaque output.

• Model selection and prompt design as demonstrable skills: instrumental AI Literacy includes knowing which model to use and how to phrase the task. Jacobsen et al. (2026) had 153 pre-service teachers' lesson-planning goals critiqued by ChatGPT-4, Claude 3 and Gemini Advanced under four systematically varied prompts (N = 240 feedbacks in Study 1, 345 in Study 2), with Feedback rated on nine quality categories. Model choice alone explained 26.9% of the variance in rated quality (18.4% in Study 2) and prompt design added a significant 15.9% (5.7%) on top; the single decisive prompt feature was domain-specific technical language, whose removal significantly lowered quality (β = −0.412), whereas adding examples and dropping the chain-of-thought instruction made no significant difference in Study 1. Their reading — that subject terminology both unlocks relevant training-data content and frames the request professionally, like role prompting — plus a rule of thumb to use the most capable current frontier model, makes model choice a didactic decision rather than a technical one.

• Ethical and critical use: recognizing bias in AI outputs, protecting student data (Privacy), and ensuring equitable outcomes (Equity). Culturally relevant AI use examines how teachers can use LLMs to diversify materials rather than reinforce dominant norms.

• Visual representation is its own measurable dimension. Chen et al. (2026) validated a five-dimension scale for generating, judging and acting on visual representations with 566 teachers across five western Chinese provinces, with subscale alpha from 0.911 to 0.934; it measures reported capacity, with no test-retest and no observation of practice.

• Ethical responsibility is distributed, not individual. Across seven national cases teachers are positioned as moral gatekeepers of AI use while lacking institutional and epistemic support, so ethical AI literacy has to extend to critical and political agency rather than technical skill (Raffaghelli et al. (2026)).

• Confidence and attitudes: teacher confidence shapes adoption. Adoption research finds confidence, support, and perceived utility drive whether teachers actually use AI, and faculty orientations shape adoption in research and teaching.

• In a five-country survey of 1,405 K-12 teachers, AI readiness predicted positive beliefs about GenAI's instructional value (B = .59, R² = .50) yet predicted greater Creativity concern (B = .18), and the same predictors explained only 7% and 6% of concern — readiness-building alone leaves concern untouched (Xiu et al. (2026)).

Readiness should be ordered and measured accordingly: the Capability–Decision Model places demonstrated AI capability upstream of attitude and confidence and proposes performance indicators rather than self-report alone — lesson-design tasks, prompt-evaluation tasks seeded with errors, content-validity judgments and classroom artifact rubrics (Mnguni (2026)).

Emotional and moral readiness is a distinct dimension. Vassallo (2026) surveyed the academic staff of a Maltese university (109 respondents) and built an AI Guilt Index (α = 0.88) from four moral-emotion items, finding that anticipatory guilt outweighed remorse experienced after use: the strongest endorsement was worry that AI use undermines one's credibility (34.9% agreeing), then feeling like one is cheating when using it (25.7%), while post-use remorse drew only 9.2%. The findings that matter for competency frameworks are that non-users reported higher guilt than users (M = 3.25 vs M = 2.32) and that guilt fell as career security rose — early-career academics reported the most (M = 2.71) and senior academics the least (M = 2.03). Emotional readiness is therefore not captured by skill or confidence measures, and the paper argues competency frameworks should treat guilt and identity concern as normal transitional responses rather than faults to correct.

A stronger version of that reframing holds that GenAI is a threshold concept rather than a skills gap: an autoethnographic account argues faculty anxiety and resistance are constitutive of threshold crossing, so skills-based training fails and principled non-adoption should be respected rather than corrected (Laidlaw (2026)).

Competency shows up as repair, not just prompting. With 7 middle school mathematics teachers rewriting 143 problems, Walkington et al. (2026) coded 415 follow-up prompts: most corrected realism, readability or mathematical structure, and six caught inappropriate or biased output — teacher judgment operating as monitoring of the generated task rather than as prompt fluency.

The competency gap

A key finding is the gap between self-reported and performance-based competency. Research on AI-literacy assessment documents a substantial discrepancy (up to ~40%) between what teachers believe they can do and what they can actually demonstrate — teachers confident in AI skills often lack foundational prompting and evaluation abilities. This motivates performance-based assessment of teacher competency rather than reliance on self-report, and connects to calibrated self-assessment. A national survey of 2,586 Nigerian secondary teachers measured the two sides with separate instruments — moderate AI awareness (M = 3.13 of 8) against lower AI pedagogical skill (M = 2.47 of 5) — with early- and mid-career teachers reporting higher skill than experienced colleagues (Olurinola et al., 2026).

Readiness is the most-documented obstacle in the mathematics literature. A review of 54 mathematics-education studies found technological–pedagogical integration and teacher readiness the most frequently reported challenge (38 of 54), ahead of assessment validity and integrity (26) and overreliance (24) (Manto et al. (2026)).

The gap is not only between perceived and actual skill but in the breadth of what teachers know: a national survey of 2,018 Ukrainian secondary educators found 84% reported using AI in professional practice while only 11% could name a specialized AI service beyond ChatGPT (Marienko, Markova & Semerikov (2026)). A second narrowness shows in what teachers volunteer rather than what they can name: 53 in-service teachers tagging their own classroom cases against the OECD AI Literacy Framework described 43 AI-literate practices against only 10 failures (Wang et al., 2026). Their competency selections clustered on practical judgment (evaluating AI outputs, N = 38) and rarely reached societal bias (N = 5), ethical alignment (N = 6) or AI's energy use (N = 9).

A 132-teacher survey in Indonesia found near-universal acceptance alongside thin mechanistic understanding: 96.2% endorsed AI use by teachers, yet only 4.6% named tokenization or decoding and 21.5% believed ChatGPT searched a database or the internet (Aji et al. (2026)). A favorable attitude is not evidence of a workable mental model of the tool.

• Use and GenAI training settle attitudes; general confidence does not. In TALIS 2024, Scandinavian teachers who had used GenAI scored 0.72 SD higher on benefit agreement and 0.26 SD lower on uncertainty about them, GenAI-specific professional development added 0.20 SD, and users also agreed with misrepresentation concerns more often (White et al., 2026). Among 425 K-12 professionals, epistemic vigilance capacity predicted calibrated AI readiness at β = .675 while autonomous motivation did not (β = .076), and only 143 reported a clear written AI policy (Song, 2026).

The gap is visible in the artifact as well as in the self-report. A cross-institutional faculty collaboratory in teacher preparation, in which teacher educators designed AI into their own methods courses, reported that candidates could produce polished AI-assisted lesson plans while being unable to explain why a plan fit the learners and the standards, since the plan itself says nothing about the reasoning behind it. Grading the justification rather than the product is one response. The pedagogies-of-practice frame suggests another, treating rehearsal as an approximation of practice: AI-mediated rehearsal with structured post-rehearsal feedback raised candidates' use of probing and exploring questions, yet candidates' own judgments of their performance still diverged from what observers recorded.

A falling self-assessment after training can signal learning rather than loss. In a six-month Intelligent-TPACK professional development program with 64 Hong Kong university teachers against 61 controls, overall AI competency rose by about half a standard deviation (Cohen's d = −.521), yet some teachers in both groups scored lower at post-test than at pre-test (Tan, Cheng & Ling, 2025). Interviews attributed the declines to a shift from unconscious to conscious incompetence — the recalibration the Dunning–Kruger effect predicts — rather than to lost skill. The authors conclude that pre-post self-report understates an intervention, because part of the gain arrives as a downward correction in the instrument itself.

The gap can invert across the pipeline. In postgraduate medical education, twelve physicians who lead AI curriculum work expected residents to become more fluent with AI than many of their teachers, and proposed co-learning and co-teaching rather than a one-way upskilling of faculty (Bursic et al. (2026)). The same interviews described informatics units not yet mature enough to lead curriculum development, which locates the constraint in institutional capacity as well as individual skill.

The gap also shows up as non-participation. Watson & Rainie (2026) surveyed 1,057 US college faculty in late 2025 and found 26% do not use generative AI tools at all, with a third choosing not to use them for teaching and non-use concentrated in the arts and humanities (40%). The institutional side of the gap was larger than the individual one: 68% said their schools had not prepared faculty to use generative AI for teaching and mentoring, and faculty named colleagues' resistance (82%) and unfamiliarity (83%) as the leading obstacles to departmental adoption — a picture in which capability-building, peer norms and policy all have to move together.

Pre-service training largely sidesteps the ethical dimension of the competency. A systematic review of AI in initial teacher training for pre-service primary mathematics teachers (Pinto et al. 2026, 11 studies selected from 341 records) found the interventions concentrated on short-term technical and pedagogical gains, with nine of the eleven running brief interventions spanning one to six sessions, and reported that ethics was addressed in only three of the eleven studies. The competency frameworks name ethical judgment as a dimension; the pre-service literature meant to build it rarely teaches or measures it.

• Seven competence clusters, with ethics the weakest link. A thematic synthesis of 78 studies yields clusters for prompt engineering, critical evaluation of AI outputs, ethical awareness and bias detection, pedagogical-content alignment, conceptual AI literacy, human oversight and reflection. Prompt engineering was the most consistently documented and behaves as a developmental spectrum rather than a single skill, which led the reviewers to argue it is best understood as pedagogical reasoning conducted through a new medium — the prompt as an externalized design decision. Ethical awareness was the weakest documented competence in the pre-service studies (Spyropoulou & Kameas, 2026).
• Teachers overestimate their own evaluation skill. Beyond the finding that only 38.3% of AI-generated learning objectives met all quality criteria without revision, pre-service teachers systematically overestimate their ability to evaluate chatbot output — which the reviewers read as a possible perceptual rather than declarative deficit. This is why they argue competence should be assessed through performance artifacts (lesson plans, decision logs, micro-teaching) rather than self-report: the unreliability of self-assessment is demonstrated in the very literature the training is meant to address.

Professional development that works

The knowledge base's PD literature identifies effective approaches:

• Intensive, theory-grounded programs: An intensive GenAI PD program with 163 teachers/pre-service teachers produced significant gains across all AI-PCK components, with pre-service teachers benefiting most. Self-determination-theory-based PD shows need-supportive training improves teachers' AI literacy, attitudes, and engagement while reducing anxiety.
• Design-based and integrated approaches: DBR-based GenAI literacy training addresses the overemphasis on technical knowledge and pre-GenAI tools; integrative, developmental frameworks and social-emotional competency integration broaden literacy beyond pure technique.
• Inquiry and authentic practice: AI-supported inquiry models build AI literacy and authentic performance in pre-service teachers.
• Context-specific readiness: The EPIQ-AI readiness framework emphasizes that faculty readiness is a sociotechnical issue requiring alignment of faculty capacity, AI Governance, and quality assurance.
• Support must be differentiated by experience and AI proficiency. Choi et al. (2026) found that how teachers actually interact with AI in lesson design depends on the interplay of teaching experience and AI proficiency, not either alone. Experienced teachers with high AI proficiency critically adapt AI output to context (re-prompting, elaboration), whereas novices — even technically fluent ones — tend to accept AI responses directly and rarely consider students and context. This argues for profiling-based PD: response-evaluation checklists and prompt templates for novices, and hands-on skill-building for experienced teachers with lower AI proficiency.
• The time benefit itself follows experience and confidence, not only support design. In Roy et al.'s (2026) trial of an AI lesson assistant, the workload reduction was larger for teachers with lower subject-knowledge confidence, lower pedagogical knowledge, less time in the profession, or higher confidence with technology. Proficiency and confidence are therefore part of the intervention's effect, not only part of its take-up.

Belief configuration is a second profiling axis: among 40,680 teachers in TALIS 2024, the same non-use status masked opposite barriers — 83.3% of the Indifferent profile cited pedagogical reservations while 66.2% of Measured Endorsement cited insufficient knowledge and skills (Fang & Jin (2026)). AI-related professional learning tracked profile membership more consistently than age or attainment.

• Teachers do not share one conception of AI. A phenomenographic study of 16 Danish higher-education teachers found three conceptions of teaching-with-AI and three of learning-with-AI, so a single workshop model reaches only part of any faculty group (Stenalt (2026)).

• Co-design and pedagogical prompt literacy are competencies, not add-ons. A systematic review of teacher–AI co-design of learning tasks (Wang, Liu & Islam 2026, 28 studies) finds the dominant collaboration mode is AI as assistant/content generator, and locates a gap in teachers' fuller co-design and dialogic partnership capacities. Talebzadeh (2026) shows PD that pairs technical AI training with pedagogical reasoning — building "pedagogical prompt literacy" (encoding PCK into prompts) — is what lets teachers turn AI output into effective differentiated group activities.

• Structures that build competency, rather than lists of it. Perl-Nussbaum and Finkelstein (2026) ran a six-session biweekly faculty learning community in a large public R1 physics department — nineteen faculty across the series, about ten at each meeting — where every session opened on local data, moved to small-group testing of AI against real anonymized student homework, and closed in collective discussion, and it produced a five-entry shared repository rather than a training package. Assessment literacy can be built the same way through a design tool: in Paula et al.'s (2026) pilot, eight experienced STEM and Health course coordinators grew their assessment literacy by critiquing a custom GPT's assessment drafts against disciplinary standards, even though the outputs repeatedly missed disciplinary context, ignored topic sequencing, and in one case carried fabricated references through repeated prompting. All eight kept academic judgment with themselves and refused end-to-end automation, which makes structured critique of generated drafts the development mechanism rather than tool training.

• Short sessions can move acceptance without moving adoption. Mesenhöller and Böhme (2026) evaluated a three-hour INSIGHT session with 100 German teachers and found perceived usefulness and perceived ease of use both rose significantly (usefulness t(99) = -3.24, p = .002, d = .32; ease of use d = .25) while behavioral intention to use AI-based technologies for differentiation did not change. The authors note their baselines were already favorable, so a ceiling effect is plausible. The reading for PD design is narrow but useful: a short session can shift how teachers judge AI tools without shifting whether they plan to use them, which is the kind of change that needs follow-through rather than a one-off event.

• Instructors prefer fluency but attribute learning to scaffold. Across 41 instructors, a GPT-4o baseline was preferred overall (21 vs 13) yet the fine-tuned three-step model (identify, explore, develop) was more often credited with learning (18 vs 13), and the two judgments agreed only 39% of the time (Molnar et al. (2026)).

• Institutional support: professional development must be paired with institutional infrastructure (policy, institutional change) for sustainable adoption.

• Authoring tools do not guarantee pedagogical fidelity. Riahi et al. (2026) studied 27 middle school teachers configuring educational chatbots in professional-development workshops, analyzing focus groups alongside configuration and interaction logs. Teachers treated the chatbot as an instructional scaffold bounded by teacher-defined rules, yet log-based alignment between intent and behavior was stronger for responsiveness (88.9%) and persona (81.5%) than for rules (70.4%) and purpose (59.3%). Configurable controls alone do not carry pedagogical intent into behavior, so expressing, testing, and refining a chatbot's pedagogy is itself a competency the authoring tooling has to support.

A design-based counterexample treats competency as situational rather than a rung on a ladder. Eyal's 2025 design-based study with 22 higher-education teacher educators had participants examine five published assessment frameworks and co-design an alternative organized around three inter-related axes: context fit (infrastructure, socio-cultural factors, local needs, developmental stage), professional needs (discipline, pedagogy, leadership, support), and dynamic development. The model rejects fixed competency levels and allows non-linear progression, and it ships with a 20-item reflective self-assessment questionnaire rated 1 to 5. Its validation is qualitative only, with no quantitative reliability testing, so it stands as a design contribution rather than a validated instrument.

• Meta-analytic estimate. Guo et al. (2026) pooled 212 effect sizes from 34 studies and estimate teacher AI-literacy PD at g = 0.76 (95% CI [0.50, 1.02]), strongest for skills (g = 0.91) and weakest for attitudes and values (g = 0.61); correcting for apparent publication selection lowers the estimate to about g = 0.49.

• Attendance predicts gains where the feeling of participating does not. In the same six-month program, attendance rate predicted the competency gain (β = .407, p = .001) while self-perceived participation did not (β = .049, p = .692), and discipline, teaching experience and professional title explained little; the authors read attendance as behavioral investment and perceived participation as surface-level engagement (Tan, Cheng & Ling, 2025).

• Values lag skills in a domain-specific program too. In a year-long Hong Kong program with 30 English teachers, knowledge of Multimodal AI assessment strategies rose from 3.13 to 4.15 out of 5 (partial η² = 0.571) and competence in supporting students from 3.05 to 3.98 (0.482), both significant, while every value subdomain stayed statistically flat under a Bonferroni-corrected alpha of .006. The teachers attributed the gains to in-school co-planning and cross-school cluster meetings rather than the workshops alone (Jiang et al., 2026).

• AI plans are a baseline, and mediation is the competency. Twenty-six beginning teachers revising AI-generated lesson plans rewrote tasks, added modeling and language supports, clarified objectives and rebuilt sequencing around classroom logistics, describing their role as reviewing and customizing rather than accepting output (Integrating AI in Lesson Planning: Critical Reflection and Thoughtful Implementation).

• Microlearning can develop university teachers' AI competence. Cechovsky (2026) refined two Vocational Education and Training modules across two online group discussions into five design principles — clear structure and orientation, guidance and navigation support, segmentation and modularization, multimodal presentation, and learner activation — and found platform navigation and locating modules the main practical barriers.

• Intention to adopt runs on need satisfaction. In a structural model of 146 teachers, competence need satisfaction predicted intention to use GenAI through intrinsic motivation and engagement in professional development (indirect 0.233), while autonomy and relatedness satisfaction did not (Yuan et al., 2026).

Teacher AI competency and the transforming teacher role

As AI takes over routine instructional and assessment tasks, the teacher's distinctive contribution shifts toward orchestration, judgment, and relationship: deciding when and how AI is used, scaffolding student agency and critical use, ensuring equity, and providing the social and emotional support AI cannot. This reframes teacher competency around human-in-the-loop oversight, ethical judgment, and supporting self-regulated learning — connecting to Teaching and guarding against over-reliance.

Implications for AI in education

• Assess performance, not just self-report: teacher competency should be evaluated through demonstration, given the documented self-report gap.

• Train the full competency, not just tools: PD should build technical, pedagogical, assessment, and ethical dimensions together, grounded in learning theory.

• Build confidence alongside skill: attitudes and Self-Efficacy shape adoption, so PD should reduce anxiety and build confidence through authentic, supported practice.

• Support the institutional layer: sustainable teacher competency requires aligned policy, governance, and capacity, not isolated training.

• Teacher digital competence for GenAI curriculum design. Guillén-Gámez (2026) validate a TAM-based diagnostic instrument with 434 in-service teachers; behavioral intention was the main predictor of digital competence for using GenAI in curriculum planning, with self-efficacy as a root driver.

A Psychometric Instrument for Teacher AI Competency

• A psychometric study developed the Teachers' AI Literacy Scale (TAILS) to measure AI literacy specifically within language teacher education, operationalizing the ED-AI framework's six dimensions. The instrument's development fills a gap in assessments that target students or general users, supporting the measurement of teacher AI competency.

The instrument landscape itself has since been reviewed. Zainal, Mohd Matore and Maat's 2026 systematic review of teacher AI literacy measurement tools appraised 33 instruments published between 2019 and 2025 and found the field methodologically monotonous: 31 (93.9%) are self-report scales of perceived confidence, only two (6.1%) test knowledge objectively, and none use performance-based tasks. Internal consistency was the strongest quality domain (28 of 33 at Grade A) and fairness the weakest, with five instruments (15.2%) reporting measurement invariance or differential item functioning evidence. Content also lags the technology, since 29 instruments (87.9%) target general AI concepts and only four (12.1%), all from 2025, address generative AI.

Common Misconceptions

• "Prompting training makes teachers' materials better." Prompting training buys time, not better materials: in a preregistered RCT with 204 teachers, Appel, Fervers and Piepenburg (2026) found ChatGPT improved time spent while the material quality did not follow. The efficiency gain is real and it is not the quality gain.
• "When teachers configure a chatbot, the failure is factual error." When teachers configure a chatbot in natural language, the dominant failure is lost intent rather than wrong facts — bots skip the step the teacher meant. Instruction-following fails structurally rather than factually.
• "Technical skill is the decisive layer." Pedagogical knowledge is the decisive layer: a cross-level study of secondary AI education Shen et al., 2026. The competence that determines the outcome is the one most training programs under-invest in.

Connected Concepts

• AI Literacy
• Educational Development
• Teaching
• Prompt Engineering
• Learning Design
• Scaffolding
• Metacognition
• Assessment Validity
• Equity
• Ethics
• Self-Efficacy
• Human-in-the-Loop
• Learner Agency
• Cognitive Offloading
• Educational AI Policy
• AI in Education
• Technological Pedagogical Content Knowledge (TPACK)
• Teacher Education
• Pedagogies and Teaching Strategies — Umbrella: pedagogies and teaching strategies in AI education

Connected Articles

• Integrating AI in Lesson Planning: Critical Reflection and Thoughtful Implementation — 26 beginning teachers revised AI lesson plans; adaptation was the largest gap (Wilcoxen et al. 2026)
• Chemistry teachers' research competence following AI-supported professional development: a mixed-methods pre-post study — Chemistry teachers’ research competence following AI-supported professional development: a mixed-methods pre–post study
• Should there be a teacher in-the-loop? A study of generative AI-personalized tasks middle school — Teacher-in-the-loop personalization of middle school mathematics tasks with ChatGPT (Walkington et al. 2026)
• Lesson planning using AI lesson assistant, Aila - A Teacher Choices Trial — Which teachers gained planning time: lower subject-knowledge confidence, lower pedagogical knowledge, newer to the profession or high technology confidence
• AI Misconception and Adoption Among Indonesian K-12 Teachers — Teachers accept AI while misunderstanding how it works (Aji et al. 2026)
• AI integration in mathematics education: A systematic review of implementation challenges and pedagogical implications (2017-2025) — Teacher readiness and technological–pedagogical integration as the most frequently reported challenge across 54 mathematics-education studies (Manto et al. 2026)
• Conceptualizing and developing second language teachers' multimodal assessment literacy in the GenAI era — Multimodal assessment literacy: a year-long program where knowledge and competence moved and values did not
• The impact of professional development programs on K-12 teachers' AI literacy: A systematic review and meta-analysis
• Enhancing teachers' AI competency: A professional development intervention study based on intelligent-TPACK framework — Intelligent-TPACK PD: half a standard deviation of gain, and negative gains that read as recalibration — The impact of professional development programs on K-12 teachers' AI literacy: A systematic review and meta-analysis
• AILitHub: Building AI Literacy Infrastructure to Situate Frameworks into Practice — Teachers tagged their own classroom AI cases and reached practical competencies readily but societal and ethical ones rarely (Wang et al. 2026)
• Typology of Generative AI Tools for Education — Tool selection as an exercise of educator agency
• Generative Artificial Intelligence (GAI) in Teaching and Learning Processes at the K-12 Level: A Systematic Review — Systematic review of generative AI in K-12 teaching and learning (Marzano 2026)
• Pedagogy first, technology second: Cross-level relationships between teacher professional knowledge and student learning in artificial intelligence (AI) education — Teacher professional knowledge in K-12 AI education: TAIK vs TPAIK and student learning (Shen et al. 2026)
• Analyzing teacher-AI interaction patterns across teacher experience and AI proficiency in student-centered lesson design — Teacher-AI interaction patterns in lesson design across experience and AI proficiency (Choi et al. 2026)
• Preparing pre-service teachers for responsible generative AI use: Curriculum implications for ethics, privacy, and AI literacy — Pre-service teachers' responsible GenAI use: curriculum implications (Kohnke et al. 2026)
• Validating AI-generated classroom observations: Reliability, accuracy, and limits of LLM-based pedagogical judgment — LLM classroom observation for teacher professional development (Melo et al. 2026)
• Responsible integration of artificial intelligence into pedagogies of practice in mathematics teacher education — Generative AI across representations, decompositions and approximations: rehearsal, structured feedback and the accuracy cautions
• Efficacy of an Intensive Generative AI Professional Development Program on Pedagogical Content Knowledge (AI-PCK) and the Comparative Analysis of Learning Gain between Experienced and Pre-service Teachers — Efficacy of an intensive GenAI professional development program
• Modeling AI-TPACK in practice: Insights from teachers' multi-agent workflow design — Modeling AI-TPACK through teacher multi-agent workflows
• Towards Synergistic Teacher-AI Interactions with Generative Artificial Intelligence — Toward synergistic teacher-AI interactions
• Teacher education for artificial intelligence literacy through a self-determination theory perspective — Teacher education for AI literacy through self-determination theory
• Development and evaluation of artificial intelligence literacy training for teacher education students — Design-based research GenAI literacy training
• Rethinking Generative AI Literacy: An Integrative, Developmental, and Dialectical Framework for K-12 Teacher Education — Rethinking GenAI literacy in teacher education
• Exploring interfaces and implications for integrating social-emotional competencies into AI literacy for education: A narrative review — Integrating social-emotional competencies with AI literacy
• AI adoption among teachers: Insights on concerns, support, confidence, and attitudes — AI adoption among teachers: confidence and support
• Faculty orientations shape adoption of AI in research and teaching — Faculty orientations shape AI adoption
• How to Assess AI Literacy: Misalignment Between Self-Reported and Objective-Based Measures — Misalignment between self-reported and performance-based AI competency
• Effects of an AI-supported inquiry model on AI literacy and authentic performance: A quasi-experimental study with preservice teachers — AI-supported inquiry for pre-service teachers
• Faculty Readiness for AI-Supported Teaching and Scalable Online Program Delivery in Higher Education: The EPIQ-AI Framework for Epistemic Integrity — EPIQ-AI faculty readiness framework
• LLMs to Support K-12 Teachers in Culturally Relevant Pedagogy: An AI Literacy Example — LLMs for culturally relevant K-12 pedagogy
• A framework for institutional change in the age of AI — Institutional change framework for AI
• TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors — TeachingCoach chatbot for instructor guidance
• When faculty ask, "what's the point of teaching?": GenAI as identity crisis, not skills gap — GenAI as identity crisis, not skills gap
• Situated AI ethics: a cultural-historical and ecological framework for education
• Transforming curriculum design with generative AI: a model for assessing teacher digital competence — Assessing Teacher Digital Competence for GenAI Curriculum Design (Guillén-Gámez 2026)
• Understanding what good education is: a phenomenographic investigation of university teachers' understandings of AI — phenomenographic study of university teachers' conceptions of AI
• AI literacy in secondary education: Framework, assessment, and professional development in the Ukrainian context — Five-level AI literacy framework + PD for Ukrainian secondary educators (Marienko et al. 2026)
• Reimagining teacher-AI co-design in learning task design: trends and perspectives — Teacher–AI co-design of learning tasks: trends and perspectives (Wang et al. 2026)
• The Architecture of Roles in AI-Designed Group Activities: A comparative inductive analysis of novice and experienced teachers' differentiated instruction within the IAT framework — Architecture of roles in AI-designed differentiated group activities (Talebzadeh 2026)
• The Impact of Explainable AI on Teachers' Trust and Acceptance of AI EdTech Recommendations: The Power of Domain-specific Explanations
• AI Literacy of Teachers: Prompt Engineering and Model Selection as Predictors of AI-Feedback Quality — Prompt engineering and model selection as predictors of AI-feedback quality (Jacobsen et al. 2026)
• The AI guilt complex: Moral emotions and ethical dilemmas in academic technology adoption — The AI Guilt Complex: anticipatory guilt and four moral response profiles among academic staff (Vassallo 2026)
• Empowering teachers to use AI for differentiation: changes in teachers’ acceptance of AI-based technologies following participation in a short professional development training session — A three-hour teacher PD session raised perceived usefulness and ease of use but not stated intention to adopt (Mesenhöller & Böhme 2026)
• Artificial intelligence in initial teacher training for pre-service primary school teachers in mathematics: a systematic review — Systematic review of AI in pre-service primary mathematics teacher training: ethics addressed in only three of eleven studies (Pinto et al. 2026)
• The AI challenge: How college faculty assess the present and future of higher education in the age of AI — AAC&U/Elon survey of 1,057 US faculty: preparedness, non-use and the individual-vs-institutional policy gap (Watson & Rainie 2026)
• AI integration as instructional design: Lessons from a cross-institutional faculty collaboratory in teacher preparation — Cross-institutional faculty collaboratory: AI integration as instructional design in teacher preparation
• Assessing teachers' AI literacy: a systematic review of measurement tools — Systematic review of 33 instruments for measuring teacher AI literacy: 31 self-report, two objective knowledge tests, no performance tasks (Zainal, Mohd Matore & Maat 2026)
• Rethinking artificial-intelligence literacy through the lens of teacher educators: The adaptive AI model — A design-based adaptive AI literacy model and 20-item reflective questionnaire co-designed with 22 teacher educators (Eyal 2025)
• A Workshop Series for Effective Use of AI in Uncertain Times: Building a Physics Faculty Learning Community — A Workshop Series for Effective Use of AI in Uncertain Times: Building a Physics Faculty Learning Community
• Designing Authentic Assessments with Generative AI: A Pilot Study of Assessment Authentifire in Higher Education — Designing Authentic Assessments with Generative AI: A Pilot Study of Assessment Authentifire in Higher Education
• Will It Teach as Intended? How Teachers Configure Educational AI Chatbots — Will It Teach as Intended? How Teachers Configure Educational AI Chatbots
• A Capability–Decision Model of teacher readiness for AI integration in teaching — Ordered capability-first teacher readiness model with performance-based capability indicators
• K-12 in-service teachers' beliefs about generative AI in classrooms: insights from the United States, India, Qatar, Colombia, and the Philippines — Cross-national survey of 1,405 K-12 teachers: readiness predicts positive beliefs but not concern
• Teachers' AI Belief Profiles and Their Associations with AI Use, Pedagogical Applications, and Barriers: A Person-Centered Analysis of TALIS 2024 — Four TALIS 2024 teacher belief profiles link professional learning and profile-specific barriers to AI use
• Designing Microlearning Modules on AI for University Teachers in Vocational Teacher Education: A Design-Based Research Study — Designing Microlearning Modules on AI for University Teachers in Vocational Teacher Education: A Design-Based Research Study
• EduWild: How Teacher-Configured Chatbots Fail in Real-World Use and How to Fix Them — EduWild: How Teacher-Configured Chatbots Fail in Real-World Use and How to Fix Them
• Development and validation of the AI-empowered visualization teaching competency scale — Development and validation of the AI-empowered teacher visualization teaching competency scale
• Exploring the integration of artificial intelligence into residency education curriculum: qualitative interviews — Barriers to integrating AI into residency curricula: qualitative interviews with twelve physician leaders
• Teachers’ basic psychological need satisfactions and their intentions to use generative artificial intelligence: the mediating role of engagement in professional development — Teachers’ basic psychological need satisfactions and their intentions to use generative artificial intelligence: the mediating role of engagement in professional development
• Scandinavian teachers' attitudes toward generative artificial intelligence: Exploration of associations — Scandinavian teachers' attitudes toward generative artificial intelligence: Exploration of associations