Reimagining adolescent education with ephegogy: generative AI and active learning in practice
Daire Maria Ní UanacháinPurpose
This paper introduces ephegogy, a theory addressing the gap between traditional educational models and adolescents' developmental needs in artificial intelligence (AI)-augmented societies. Blending constructivism, connectivism and generativism, it explores how integrating generative AI (GenAI) and active learning can scaffold motivation, autonomy, and relevance for adolescent learners.
Design/methodology/approach
A mixed-methods case study conducted in a single secondary-school geography course during 2024–2025. Quantitative data came from matched pre- and post-intervention surveys measuring motivation and self-efficacy (N = 17; N = 11 for the self-efficacy composite) and locus of control (N = 12), using instruments adapted for an under-14, predominantly English-as-an-additional-language cohort. A separate matched engagement survey (N = 24) and an end-of-course co-creation survey (N = 33) provided further data. Qualitative data comprised 74 individual student reflections analysed thematically using the cultural-historical activity theory (CHAT) framework, alongside open-text survey responses from 74 students. The study was designed using three frameworks (self-determination theory, ephegogy and CHAT).
Findings
Findings are associational and exploratory, specific to this pilot. Composite motivation increased significantly (p = 0.043, d = 0.53), led by a significant rise in school enjoyment (p = 0.028). On a separate instrument, confidence in using AI tools showed the largest single increase (p = 0.016) and motivation relative to traditional courses also rose (p = 0.040); as several items were examined, these are treated as exploratory. Self-efficacy and locus of control showed no reliable change. Qualitatively, choice, the learning contract, and resource autonomy were dominant themes; most students disclosed and reflected on their AI use, and the modal student experience was ambivalent, valuing autonomy while finding it demanding.
Research limitations/implications
As a single pilot case study with small, matched samples, no control group and a single teacher-researcher as sole coder, findings are context-specific and cannot support causal claims. Future research should evaluate the framework with larger samples, control conditions, and across diverse cultural contexts and disciplines.
Practical implications
Ephegogy provides actionable strategies for scaffolding student autonomy and ethical AI use within project-based learning. Tools such as the Student-Author Voice (SAV) rubric support metacognitive reflection and responsible co-creation, assessing authored voice rather than relying on AI detection.
Social implications
Ephegogy provides actionable strategies for scaffolding student autonomy and ethical AI use within project-based learning. Tools such as the SAV rubric support metacognitive reflection and responsible co-creation, assessing authored voice rather than relying on AI detection.
Originality/value
Ephegogy reconceptualises adolescent education for the AI era as a distinct developmental stage requiring its own framework, operationalised through gradual immersion and the assessment of authored voice. This paper reports an initial classroom pilot offering preliminary, transferable insights rather than confirmatory evidence.