A systematic review and meta-analysis of randomized controlled trials brings computer-assisted learning platforms and generative AI tools into a common framework under common inclusion criteria and on a common effect-size scale, finding no evidence that generative tools outperform the technologies that preceded them.
Overall, the results show that while home-based delivery of educational technology can enhance scalability, institutional involvement remains critical for translating scalable technology into learning gains.
Juanita Bloomfield, A. Balsa, Alejandro Cid· 0 citations
In a preregistered randomized field experiment in a graduate corporate-finance course of an online MBA, 86 students were randomized between a structured tutor grounded in the course materials and a holdout in which consumer AI remained freely available.
Shi-Hao Yang, Marshall W. van Alstyne, Chrysanthos N. Dellarocas· 0 citations
To ensure a responsible transition to AI-assisted education, this study recommends a phased strategy: upgrading IT infrastructure, providing regular AI literacy training for educators, and establishing structured faculty supervision protocols to mitigate student dependency.
Raihand Ramadhani Abdul Sayeed, Emi Sita Eriana, Afrizal Zein· bit-Tech· 0 citations
This work argues that self-paced settings strip away the implicit scaffolding of the classroom, so unguided LLM access defaults to cognitive offloading, metacognitive disengagement, and fragmented learning that erodes durable competence and argues that resourcing the protective environment is, in effect, educational an...
H. Jamil· Big Data and Cognitive Compu...· 0 citations
It is concluded that ITSs should function as human-guided instructional resources rather than autonomous replacements for teachers, and their responsible implementation requires pedagogical and curricular alignment, teacher preparedness, accessible infrastructure, valid assessment, transparent data governance, and cont...
I. Ogunsakin, M. A. Ayanwale, O. Falebita et al.· Frontiers in Education· 0 citations
This dissertation examines AI-powered personalized learning during primary school fraction instruction, a domain that is foundational to later mathematics and STEM achievement and contributes classroom-based evidence to ongoing discussion about the capabilities and limits of adaptive AI systems in elementary mathematic...
Kenneth Holman· 1 citation
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