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Beyond Engagement: A Human Flourishing Framework for Emotion-Aware AI in Education

Oct 2026 · European Conference on e-Learning · 0 citations · 25 references

Abstract

Emotion-aware artificial intelligence (AI) is increasingly used to infer learners' affective states, such as frustration, confusion, or engagement, and to adapt instruction in response. These capabilities may make learning environments more responsive, but they also raise questions about privacy, surveillance, bias, and the treatment of emotional experience as data. Evaluation has therefore moved beyond accuracy alone. Trustworthy AI stresses transparency and oversight; AI Ethics adds fairness and harm prevention; Human-Centred AI foregrounds learner agency. These perspectives are necessary, yet they do not fully settle a further educational question: against what longer-term human purpose should emotion-aware AI be judged? This conceptual paper proposes the Human Flourishing Framework (HFF) as a normative evaluative perspective for addressing that question. It is not presented as a computational layer or a causal model. Instead, it examines the conditions created around AI-mediated learning within a wider educational ecosystem. The framework organises five dimensions, Emotional Autonomy, Psychological Safety, Transparency and Data Dignity, Fairness and Inclusion, and Human Flourishing, across Foundation, Empowerment, and Outcome layers. Its purpose is to examine whether the design and use of emotion-aware AI support or constrain learners' longer-term development, without attributing flourishing to the technology itself. The paper clarifies the framework's epistemological and theoretical positioning, distinguishes flourishing from related concepts such as capability, agency, and self-regulation, and translates the HFF dimensions into indicative system requirements. The contribution is conceptual rather than empirical. The framework has not yet been validated or operationalised as a measurement model; future work will therefore require longitudinal, multi-level, and cross-cultural research capable of studying AI-mediated learning within its wider educational context.

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