LDA topic modeling of 242 LDT syntheses reveals eight thematic areas, with computational tools most prevalent (21.7%) and generative AI integration emerging (8.2%)¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Latent Dirichlet Allocation on titles and abstracts of 242 LDT syntheses identified eight prominent thematic areas, led by computational tools, robotics, and programming (21.7%, n = 53), with generative AI integration in education smallest (8.2%, n = 20). → Xiyu Wang 2026

Evidence¶

Xiyu Wang 2026¶

Xiyu Wang, Fatemeh Dadashipour, Basori, Yukiko Maeda, & Jennifer C. Richardson. (2026). Scaffolding systematic reviews in learning design and technology through mentoring and AI integration. Educational technology research and development. https://doi.org/10.1007/s11423-026-10629-8

q2 · i? · review · r2

An LDA analysis of titles and abstracts from the 242 catalogued syntheses "revealed eight prominent thematic areas" with the printed percentages and counts for each, from computational tools (21.7%, n = 53) to generative AI integration (8.2%, n = 20).

"Our LDA analysis 1 revealed eight prominent thematic areas: (1) Computational tools, robotics, and programming (21.7%, n = 53), (2) adaptive and individualized instructional design (14.8%, n = 36), (3) Online learning, learner support, and collaboration (12.7%, n = 31), (4) Student learning outcomes and assessments (11.07%, n = 27), (5) Immersive and virtual learning environments (11.07%, n = 27), (6) Game-based learning, gamification, and pedagogical design (10.25%, n = 25), (7) Applications of technology in teaching and instructional design (10.25%, n = 25), and (8) Generative AI integration in education (8.2%, n = 20)."

Discussion¶

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