Class Integration of ChatGPT and Learning Analytics for Higher Education.

Abstract

Background: Active Learning with AI-tutoring in Higher Education tackles dropout rates. Objectives: To investigate teaching-learning methodologies preferred by students. ChatGPT-based gamified learning methodology is compared to another active learning methodology and a traditional methodology. Study with Learning Analytics to evaluate alternatives, their implementation, and help students elect the best strategies according to their preferences. Methods: Comparative study of three learning methodologies in a Single-Group counterbalanced with 45 university students. It follows a pretest/post-test approach using AHP and SAM. HRV and GSR used for emotional state estimation. Findings: Criteria related to in-class experiences valued higher than test-related criteria. Chat-GPT integration was well regarded compared to well-established methodologies. Student emotion self-assessment correlated with physiological measures, validating used Learning Analitycs. Conclusions: Proposed model AI-Tutoring classroom integration functions effectively at increasing engagement and avoiding false information. AHP with the physiological measuring allows students to determine preferred learning methodologies, avoiding biases, and acknowledging minority groups.