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Integrating AI-Based Sentiment Analytics and Emotional Intelligence to Predict Academic Achievement among Secondary School Students

Year: 2026
Type: article
Study design: Observational Study
Abstract: ABSTRACTThe growing use of Artificial Intelligence (AI) in education has made it possible to look athow students learn by using data-driven methods like sentiment and learning analytics. AI-based analytics can forecast academic outcomes by detecting engagement patterns;nevertheless, students' emotional capabilities may affect the efficacy of these insights inenhancing performance. The current study investigated the predictive influence of AI-drivensentiment and learning analytics on academic performance among secondary school pupils,with emotional intelligence regarded as a mediating variable.A quantitative descriptive correlational design was utilized. The sample comprised 200students from Classes IX and X, chosen via random sampling. AI-generated sentiment andlearning analytics metrics were extracted from structured five-point learning experienceratings that indicated interest, engagement, and feedback response in AI-enhanced scienceeducation settings. A standardized scale was used to measure emotional intelligence, andannual exam scores were used to measure academic success. Descriptive statistics, Pearson'scorrelation, and regression-based mediation analysis were used to look at the data.The results indicated substantial favourable correlations between AI-derived sentimentindicators, emotional intelligence, and academic performance. Emotional intelligencepartially mediated the association between AI-derived indicators and academic achievement,indicating that emotional competences affect students' responses to analytics-informedlearning feedback. The research underscores the significance of amalgamating emotional skillenhancement with AI-driven learning analytics to foster equitable, human-centric educationalmethodologies.
DOI: 10.5281/zenodo.22122771
Institution: Alagappa University
Language: English
Open Access status: green
OpenAlex ID: W7204448323
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