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A systematic review of generative AI in education: Empirical insights from a human–AI interaction perspective

● high · 2026-08-08systematic-review, generative-ai, human-ai-interaction, ai-literacy, higher-ed, efficacy-study

Synthesis: Liang, Yang, Sha, Gašević, Yan & Chen (2026) systematically review 56 empirical studies on GenAI in education through the AIED-HCD framework, analyzing three human–AI interaction modes along dimensions of human control and AI automation. They find that practice remains cautious toward high-AI-automation modes, but a high-control + high-automation mode is emerging as a trend — suggesting the future is not AI replacing humans but calibrated human–AI complementarity.

This BJET review synthesizes 56 empirical studies on GenAI in education, uniquely applying the AIED-HCD framework which conceptualizes human–AI interaction along two dimensions: human control and AI automation. Three interaction modes emerge: (1) low AI automation + high human control (teacher-led), (2) balanced, and (3) high automation + high human control (emerging trend). A sensitivity analysis validates the robustness of findings across modes. The review identifies that while practice remains cautious toward high-automation modes, the simultaneous presence of high human control with high AI automation represents a promising direction.

  • 56 empirical studies systematically reviewed through AIED-HCD human–AI interaction framework
  • Three interaction modes identified along human control × AI automation dimensions
  • High-control + high-automation mode emerging as promising direction — not AI replacement but complementarity
  • Most current practice remains in lower-automation modes with strong teacher/learner oversight
  • Sensitivity analysis confirms findings robust across interaction modes

    • Connected Concepts

  • Prompt Engineering
  • Affective Tutoring
  • Automated Essay Scoring
  • Curriculum Design
  • Plagiarism Detection
  • Student Experience
  • Administrator
  • Equity In AI Education

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    • Citation

    Liang, Z., Yang, K., Sha, L., Gašević, D., Yan, L., & Chen, G. (2026). A systematic review of generative AI in education: Empirical insights from a human–AI interaction perspective.