AI in the Classroom: A Systematic Review of Barriers to Educator Acceptance
Bibliographic record
Abstract
This study investigates the barriers to educator acceptance of Artificial Intelligence (AI) technologies in education through a systematic review guided by the PRISMA 2020 framework. With educators occupying a pivotal role in the classroom as facilitators of learning and mediators of technology use, their acceptance and integration of AI tools are critical to the success of educational innovation. Educators' readiness and resistance to AI are examined through the synthesis of empirical findings from peer-reviewed studies published between 2020 and 2025. From an initial 404 records identified, 310 remained after duplicate removal. Following title and abstract screening, 33 records were retained. After a full-text eligibility review, 14 studies were included in the qualitative synthesis, of which 10 met the criteria for final analysis. The results highlight that demographic factors such as age, gender, and digital literacy significantly affect educators' readiness to use AI. Common barriers include insufficient training, infrastructure limitations, ethical concerns, anxiety, and perceived misalignment between AI tools and pedagogical goals. Barriers vary by regional and institutional context. Developing countries face technological and resource-based challenges, while developed nations encounter pedagogical and ethical issues. The study compares several theoretical models, including the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), to explain variations in AI adoption, further integrating perspectives on emotional response, professional identity, and institutional culture. This review provides critical insights for educational policymakers, leaders, and technology developers to design inclusive, ethically sound, and pedagogically aligned strategies for AI integration in classrooms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.087 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".