Using Artificial Intelligence to Promote Adolescents' Learning Motivation. A Longitudinal Intervention From the Self‐Determination Theory

• Published in: Journal of computer assisted learning Vol. 41; no. 2
• Main Authors: Galindo‐Domínguez, Héctor, Delgado, Nahia, Urruzola, María‐Victoria, Etxabe, Jose‐María, Campo, Lucía
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01.04.2025; Wiley Subscription Services, Inc
• Subjects: Adolescents; Artificial intelligence; Autonomy; Competence; Education; Intervention; Learning; Learning Motivation; Longitudinal studies; Motivation; Psychological Needs; Secondary education; Secondary School Students; Self Motivation; self‐determination theory; Structural Equation Models; Student Motivation; Students
• ISSN: 0266-4909; 1365-2729
• DOI: 10.1111/jcal.70020

ABSTRACT Background With the integration of artificial intelligence into educational processes, its impact remains to be discovered. Objective The aim of the present study was to determine whether, after a 7‐month intervention in which a subject of artificial intelligence was taught, students improved their psychological needs for competence, autonomy and relatedness, potentially leading to an increase in their intrinsic motivation towards learning. Additionally, the study examined the impact of students' use of ICT and the influence of gender along the intervention. Methods This longitudinal study included a total of 50 adolescents from Secondary Education, who responded to a series of scales to measure the main constructs of perceived competence, autonomy, relatedness and intrinsic motivation at two different times (T1 and T2). Results The results showed that, regardless of gender and the frequency of academic or non‐academic use of ICT, statistically significant improvements were observed only in the need for relatedness. Likewise, an analysis of structural equation models revealed that students' initial competence (T1) was the main predictor of their initial motivation (T1), and having this initial motivation was essential for further improving motivation after the intervention (T2). Similarly, each basic psychological need at its initial time point (T1) significantly predicted that same psychological need at its final time point (T2), with considerably high explained variances. Conclusions These results shed some light on the potential effect that AI‐based interventions can have on the basic psychological needs of secondary education students.