AI Ed Wiki logoAI Ed Wiki
Use with AI

📄 Research Article

Integrating Generative Artificial Intelligence into Student Learning: A Systematic Review from a TPACK Perspective

● high · 2026-08-14 · generative-ai, tpack, ai-tpack, student-experience, higher-ed, k-12, instructional-design, assessment, ai-literacy, meta-analysis, systematic-review, teacher-ai-competency, faculty-development

Synthesis: Liu and Zhong (2025) systematically review 71 high-quality empirical studies (74 effect sizes) of generative AI integration into student learning, published after ChatGPT's release (November 30, 2022), through the lens of the TPACK framework. A random-effects meta-analysis finds a medium-to-large overall effect on learning outcomes (Hedges' g = 0.752, 95% CI [0.566, 0.937]), with strong cognitive (g = 0.831) and affective (g = 0.729) gains but a negligible effect on behavioral engagement. The review identifies two critical priorities: developing students' GenAI literacy and establishing GenAI-TPACK professional development for teachers.

Key Findings

  • Overall positive effect. Across 47 journal articles and 74 effect sizes, learners using GenAI outperformed control groups (Hedges' g = 0.752, p < 0.001), a medium-to-large effect.
  • Domain-specific outcomes. GenAI showed a strong effect on cognitive outcomes (SMD = 0.831, 95% CI [0.596, 1.066]) and a medium-to-large effect on affective outcomes (SMD = 0.729, 95% CI [0.414, 1.044]), but only a small, non-significant effect on behavioral engagement (SMD = 0.057, p = 0.828).
  • Uneven adoption. GenAI is more widely applied in higher education than in K-12; quasi-experimental designs dominate; most studies focus on pedagogical development rather than curriculum development.
  • GenAI as a tool role. GenAI most often acted as a learning tool, with tool roles varying by discipline. Pedagogical scaffolding of GenAI use was frequently incomplete.
  • Moderating variables. Research method/focus and key instructional-design variables (learning content, tool, strategy, assessment, outcome) moderated learning outcomes.
  • Two critical priorities. (1) GenAI literacy development for students, and (2) GenAI-TPACK professional development for teachers.

Research Landscape

The 71 studies span publishing year, country/region, authorship type, sample size, learning stage, educational setting, duration, and research method. GenAI-integrated student learning has attracted global attention, is concentrated in higher education, and is dominated by quasi-experimental designs that require larger samples and longer durations than other methods. The review emphasizes the need for international collaboration, exploration of pedagogical strategies for K-12 higher-order thinking, more rigorous controlled experiments, and systematic curricula for introducing GenAI.

The TPACK Lens

The review applies the TPACK framework to identify what knowledge teachers need to integrate GenAI effectively into student learning. It synthesizes GenAI-related competencies across the framework's domains — technological knowledge (operating GenAI, examining its benefits/limitations and ethical implications), pedagogical knowledge (scaffolding student use), and content knowledge (integrating AI within subject-matter instruction). This positions GenAI-TPACK professional development as a core requirement for realizing AI's benefits while mitigating misuse risk, connecting to the wiki's Teacher AI Competency and Faculty Development research.

Design Implications

  1. Scaffold GenAI use deliberately. Given incomplete pedagogical scaffolding across studies, GenAI integration must pair tool access with structured pedagogical support.
  2. Develop student GenAI literacy. Teaching students to understand, evaluate, and responsibly use GenAI is a critical, underexplored priority.
  3. Build GenAI-TPACK through professional development. Teachers need integrated technological-pedagogical-content knowledge, not isolated tool training.
  4. Broaden beyond higher education. GenAI's application in K-12 and the development of systematic AI curricula remain gaps.

Connected Concepts

Connected Articles

Citation

Liu, X., & Zhong, B. (2025). Integrating generative Artificial Intelligence into student learning: A systematic review from a TPACK perspective. Educational Research Review, 49, 100741.