Abstract:

Increasing heterogeneity in school classes and a shortage of teachers ensure the increasing importance of self-learning phases for pupils. Motivation is essential for designing these phases efficiently and achieving learning progress. AI learning assistants enable individual and self-directed learning adapted to the needs of the learners.

From March to May 2023, the AI Education (AIEDN) research project investigated how an AI-based learning assistant can compensate for existing impediments by enabling a better understanding through video-based learning. For this study, 275 students aged 14-20 were selected from two secondary schools (N=137) and two grammar schools (N=138) in Baden-Württemberg, Germany. The experiment tested the extent to which learners solve more tasks, build broader (transfer) knowledge, and retain it. The learning assistant works based on semantic AI, understanding questions’ meanings and displaying matching passages from the videos of math YouTuber Daniel Jung.

To investigate the motivation potential of the pupils, 21 qualitative semi-structured interviews were conducted with randomly selected participants of the AIEDN study. The analysed interviews were placed in the framework of the ARCS (Attention, Relevance, Confidence, Satisfaction) model of motivation, which enables a closer look at different facets.

The work results show that the AIEDN AI Learning Assistant can contribute to increased motivation to learn. New functions and relevant support are the most critical aspects. Analyzing the motivational potentials also shows that these significantly influence the overall perception of an AI learning environment. The ARCS model can support the systematic implementation of motivation-enhancing functions. However, this should be adapted to the individual circumstances of the respective AI learning assistant.

Keywords:

Motivation, learning, artificial intelligence, AI learning assistants, motivational potential, learning environment, attention, relevance, confidence, satisfaction.