AI is predicted to significantly impact education, particularly in teacher training and professionalization. This proposal examines the shift from teacher training through technologies to training within technologies, focusing on the metaverse as an AI-driven environment. It explores the teacher-learner rela-tionship as a mediator of educational action, analysing the challenges and op-portunities posed by Intelligent Tutoring Systems (ITS) in immersive training environments. Using the CLAS-WE approach (Efron & Ravid, 2019), a critical synthesis of systematic reviews on relationality in intelligent tutoring within im-mersive environments is presented. The study aims to clarify key terms and concepts linked to the teacher-student relationship and intelligent tutoring in environments like the metaverse. It addresses the research question: what characteristics does the teacher-learner relationship present in systematic re-views on ITS in immersive environments? The synthesis highlights potentials (e.g., personalized feedback) and risks (e.g., curriculum alienation) associated with these relationships. The findings contribute to the debate on teacher train-ing in immersive contexts, emphasizing the need for pedagogical oversight to safeguard key teaching-learning elements (e.g., relationships, curriculum de-sign). The work underscores the importance of carefully designed immersive environments to optimize educational outcomes and mitigate risks.
DEMINING THE FOUNDATIONS OF THE TEACHING-LEARNING PROCESS. SCOPING REVIEW ON OPPORTUNITIES AND RISK FOR TEACHING PRACTICES SUP-PORTED BY INTELLIGENT TUTORING SYSTEM
Agrati L. S.
2025-01-01
Abstract
AI is predicted to significantly impact education, particularly in teacher training and professionalization. This proposal examines the shift from teacher training through technologies to training within technologies, focusing on the metaverse as an AI-driven environment. It explores the teacher-learner rela-tionship as a mediator of educational action, analysing the challenges and op-portunities posed by Intelligent Tutoring Systems (ITS) in immersive training environments. Using the CLAS-WE approach (Efron & Ravid, 2019), a critical synthesis of systematic reviews on relationality in intelligent tutoring within im-mersive environments is presented. The study aims to clarify key terms and concepts linked to the teacher-student relationship and intelligent tutoring in environments like the metaverse. It addresses the research question: what characteristics does the teacher-learner relationship present in systematic re-views on ITS in immersive environments? The synthesis highlights potentials (e.g., personalized feedback) and risks (e.g., curriculum alienation) associated with these relationships. The findings contribute to the debate on teacher train-ing in immersive contexts, emphasizing the need for pedagogical oversight to safeguard key teaching-learning elements (e.g., relationships, curriculum de-sign). The work underscores the importance of carefully designed immersive environments to optimize educational outcomes and mitigate risks.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
