Implementing Learning Principles with a Personal AI Tutor: A Case Study

Effective learning strategies based on principles like personalization, retrieval practice, and spaced repetition are often challenging to implement due to practical constraints. Here we explore the integration of AI tutors to complement learning programs in accordance with learning sciences. A semester-long study was conducted at UniDistance Suisse, where an AI tutor app was provided to psychology students taking a neuroscience course (N=51). After automatically generating microlearning questions from existing course materials using GPT-3, the AI tutor developed a dynamic neural-network model of each student's grasp of key concepts. This enabled the implementation of distributed retrieval practice, personalized to each student's individual level and abilities. The results indicate that students who actively engaged with the AI tutor achieved significantly higher grades. Moreover, active engagement led to an average improvement of up to 15 percentile points compared to a parallel course without AI tutor. Additionally, the grasp strongly correlated with the exam grade, thus validating the relevance of neural-network predictions. This research demonstrates the ability of personal AI tutors to model human learning processes and effectively enhance academic performance. By integrating AI tutors into their programs, educators can offer students personalized learning experiences grounded in the principles of learning sciences, thereby addressing the challenges associated with implementing effective learning strategies. These findings contribute to the growing body of knowledge on the transformative potential of AI in education.Comment: 17 pages, 7 figure

Éléments de validité à propos d’un instrument mesurant le climat relationnel perçu par des étudiants et étudiantes universitaires à distance

Le questionnaire Climat relationnel des études à l’Université (CREU) (Genoud, 2008) a été adapté en français et en allemand et intitulé Climat relationnel des études à distance (CRED). Une analyse factorielle confirmatoire (n = 223) soutient la structure du CRED en quatre sous-échelles. D’autres éléments de validité sont fournis par des coefficients de cohérence interne et par la comparaison avec les résultats antérieurs obtenus par Genoud (2008). Toutefois, une faible invariance de mesure entre les échantillons (francophones et germanophones) et des indices d’ajustement parfois insuffisants, en particulier pour la version allemande, invitent à la nuance. La validité de critère est étayée par des liens significatifs positifs entre le sentiment d’auto-efficacité, la persévérance et la qualité perçue du climat relationnel (CRED). Des liens négatifs ont été trouvés entre le CRED et la propension à l’ennui (SBPS). Aucun lien n’apparait entre le climat relationnel et les performances (notes).To provide a tool to measure classroom climate in the specific context of distance learning, the Relational Climate of University Study (CREU ; Genoud, 2008) questionnaire was adapted in French and German. We named it the Relational Climate of Distance Learning (CRED). The four-scale structure of the CRED (and of the CREU) was confirmed by confirmatory factor analysis (n=223) and internal consistency analyses. These elements of validity are compared with the previous results obtained by Genoud (2008). However, a weak measurement invariance between the samples (French and German) and sometimes insufficient fit indices, in particular for the German version, call for nuance. Investigation of the psychometric qualities of the CRED was supplemented by criterion validity tests. Consistent with previous work, significant positive medium-sized relationships were found between perceived quality of classroom relational climate (CRED), feelings of self-efficacy, and persistence. No relationship was found with academic performance (i.e. grades).O questionário Clima relacional de estudos universitários (CREU ; Genoud, 2008) foi adaptado para francês e alemão e intitulado Clima relacional de estudos à distância (CRED). Uma análise fatorial confirmatória (n=223) apoia a estrutura do CRED em quatro subescalas. Outros elementos de validade são fornecidos pelos coeficientes de consistência interna e pela comparação com resultados anteriores obtidos por Genoud (2008). No entanto, uma baixa invariância de medição entre as amostras (francófona e alemã) e índices de ajustamento por vezes insuficientes, em particular para a versão alemã, convidam a ter em atenção as diferenças mais subtis. A validade do critério é apoiada por relações significativas positivas entre o sentimento de autoeficácia, a perseverança e a qualidade percebida do clima relacional (CRED). Foram encontradas relações negativas entre o CRED e a propensão ao tédio (SBPS). Não aparece nenhuma relação entre o clima relacional e os desempenhos (notas)