The impact of neuroscience and artificial intelligence on feedback: a systematic review


Adiguzel O. C., Potvin P., ESEN E.

Educational Technology Research and Development, 2026 (SSCI, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1007/s11423-026-10589-z
  • Dergi Adı: Educational Technology Research and Development
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, IBZ Online, Education Abstracts, Educational research abstracts (ERA), ERIC (Education Resources Information Center), INSPEC, MLA - Modern Language Association Database, Psycinfo
  • Anahtar Kelimeler: Artificial intelligence, Brain imaging technologies, Feedback, Neuroscience, Teaching and learning
  • Anadolu Üniversitesi Adresli: Evet

Özet

In recent years, significant advances in brain imaging technologies, artificial intelligence (AI), and neuroscience have significantly improved our understanding of how the brain functions in the learning process. This research aims to explore the role of neuroscience and AI in designing effective feedback mechanisms by investigating their impact on learning-teaching processes. To achieve this goal, the study addresses the following questions: "1) What are the objective orientations, thematic patterns, methodological approaches, and key findings of neuroscience research investigating feedback processes?" and "2) What are the objective orientations, thematic patterns, methodological approaches, and key findings of AI research investigating feedback processes?" This research adopts a systematic review methodology that encompasses four key stages: planning, searching, selection, and synthesis. Using specific keywords, relevant studies in educational sciences, educational psychology, and neuroscience were systematically identified from the Web of Science (WOS) database, specifically targeting publications indexed in SSCI, ESCI, and SCI-E. Through comprehensive thematic analysis, we systematically mapped the research landscapes in both domains. The systematic review reveals that neuroscience and AI research provide complementary insights into feedback effectiveness. The findings suggest that optimal feedback design requires integrating neurologically-informed principles with AI-enabled delivery systems to create developmentally appropriate and individually adaptive learning environments. This study further highlights a notable deficiency in interdisciplinary research that integrates neuroscience and AI approaches to optimize feedback for performance and learning. Limited collaboration between these fields hinders knowledge exchange and prevents mutual enhancement of feedback methodologies. These fields have developed independently with limited integration and a lack of comprehensive theoretical exploration and investigation into various feedback types and strategies.