Research Summary: From self-regulated to self-determined learning: identifying the heutagogical gap and designing GenAI scaffolds in asynchronous higher education

Research identifies a 'heutagogical gap' in asynchronous online higher education, representing the developmental space between self-regulated learning (SRL) and self-determined learning. The study, based on an analysis of preservice teachers' learning behaviors, proposes that Generative AI (GenAI) can act as a scaffolding tool to bridge this gap and foster self-determined learning, particularly in environments requiring high learner independence.

Why it matters

This research is strategically important because it addresses the challenge of fostering deeper learning autonomy in scalable online educational models. By identifying a specific gap and proposing a technological solution, it highlights pathways for enhancing educational outcomes and preparing learners for increasingly independent learning and professional environments.

Key insights

• Asynchronous online courses necessitate high learner independence, but their structural flexibility does not automatically lead to self-determined learning.
• The study defines a 'heutagogical gap' as the developmental difference between self-regulated learning (SRL) and self-determined learning.
• A qualitative analysis of 304 SRL-coded meaning units from 75 preservice teachers was conducted.
• Data was coded using a 0–3 heutagogical-gap scale to identify patterns.
• The identified patterns were used to design a GenAI prompt repository intended to scaffold heutagogical development, presented as a design output rather than a tested intervention.

Source

Frontiers in Education — https://www.frontiersin.org/articles/10.3389/feduc.2026.1895481