Toward a Digital Emotional-Intelligence Tool for Faculty Motivation: A Self-Determination Theory-Grounded Feasibility Study of Theory-Guided Message Personalization

Abstract

Most computational work on emotional intelligence in education targets students; where the emotional states of teaching staff are modeled at all, the signal is typically a static questionnaire score rather than an input to an automated response. This study asks a narrower question: does routing a detected emotion through Self-Determination Theory—rather than naming the emotion, or using generic language—produce a message that university faculty themselves rate as better, and does that advantage survive once the underlying emotion classifier is realistically imperfect? Using two public emotion corpora (GoEmotions and ISEAR), a 75-item researcher-authored and independently unvalidated set of faculty-oriented stress vignettes, four classifier families spanning lexicon counting to large-language-model few-shot prompting, a four-level message-personalization ablation (generic, emotion-only, need-only, full situational context), and blind ratings from 30 higher-education faculty and researchers, two findings are reported. First, when classifiers are compared on a matched label space, the few-shot large-language-model classifier performs best on both out-of-domain evaluations without fine-tuning, including 0.893 accuracy on the synthetic faculty-oriented vignette set. Second, the human evaluation does not support a uniform monotonic benefit from progressively adding personalization. Need-only routing does not yield a consistent gain over emotion-only messages, whereas the full-context condition shows its clearest positive signal for relevance and motivational usefulness; these two contrasts are significant in the crossed mixed-effects analysis but are not uniformly significant under the rater-level paired tests. The automatic text-similarity proxy does not detect this contextualized-condition advantage. Because the faculty vignettes are synthetic and were not independently content-validated, the results should be interpreted as evidence of technical and methodological feasibility rather than ecological validity or deployment effectiveness.