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Neuro-Adaptive Blended Learning in AI – Rich Environments: Research Focus, Outcomes and the Road Ahead

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  • Sivasankar A

    (Principal Alpha Arts and Science College, Chennai, Tamilnadu, India)

Abstract

Right now, smart software learns as students do, shifting the balance away from rigid teaching formats. Instead of making learners fit the method, some tools adjust mid-step using brain research plus digital feedback loops. Outcomes begin standing out when classroom work blends with adaptive platforms tuned live through behaviour patterns. One analysis pulled findings from multiple experiments and big-picture summaries to track what happens behind the scenes. Results show stronger memory recall, sharper test outcomes, better mental effort control - around one-quarter to over one-third improvement across cases. Still, concerns about who owns data, upkeep expenses, fairness in automated choices pop up every time progress appears solid. Progress stalls unless tech grows hand-in-hand with proven education methods and access for all shapes of classrooms.

Suggested Citation

  • Sivasankar A, 2026. "Neuro-Adaptive Blended Learning in AI – Rich Environments: Research Focus, Outcomes and the Road Ahead," International Journal of Latest Technology in Engineering, Management & Applied Science, RSIS International, vol. 15(6), pages 3852-3860, July.
  • Handle: RePEc:bjf:ijltem:v:15:y:2026:i:6:a:3106
    DOI: 10.51583/IJLTEMAS.2026.150600285
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