A Systematic Review of Interaction in Teaching and Learning with Artificial Intelligence (AI)

Abstract

Addressing a knowledge gap in higher education pedagogical research, this study examines student and teacher interaction with Artificial Intelligence (AI) following the rise of tools like ChatGPT. Through a systematic review of 33 studies from SCOPUS and Web of Science, the research utilizes established frameworks to codify the types, factors, and effects of AI-driven interaction in teaching and learning. Results indicate that while AI and distance education are becoming standard, there is a critical need for further research into the challenges and diversity of these interactions, specifically through the lens of established theories. Current trends show that AI is frequently utilized for personalized learning; however, findings suggest this may inadvertently diminish social interaction and collaborative engagement. Furthermore, the review reveals that AI is often relegated to relationship building or basic dialogue, highlighting a need for more robust applications in feedback and instructional guidance roles. This work contributes to the field by decomposing interaction into measurable variables and providing a holistic map of the current landscape. Ultimately, it identifies essential areas for future research to ensure that AI integration enhances, rather than isolates, the pedagogical experience.