Machine learning and generative artificial intelligence in university learning analytics: applications, trends and challenges from a narrative review
DOI:
https://doi.org/10.63969/hw5sbz05Keywords:
learning analytics, machine learning, generative artificial intelligence, higher education, algorithmic ethicsAbstract
Introduction: University learning analytics has evolved from describing digital records to predictive models and generative systems capable of interpreting and communicating educational information. Its expansion creates pedagogical opportunities and ethical risks. Methodology: A narrative review of scientific literature on machine learning, generative artificial intelligence and learning analytics in higher education was developed. Documentary synthesis was organised around conceptual foundations, applications, trends, challenges and conditions for institutional adoption, without conducting a meta-analysis or claiming original experimental findings. Results: The literature describes machine-learning applications for predicting performance, identifying dropout risk, characterising engagement and supporting tutoring decisions. Generative artificial intelligence extends the possibilities of summarising interactions, interpreting discourse and producing contextualised feedback; nevertheless, its outputs require validation and human oversight. Limitations include data quality and representativeness, algorithmic bias, explainability, privacy, student autonomy and curricular integration. Dashboards and alerts become useful when connected to timely, assessable pedagogical interventions. Conclusions: Combining machine learning and generative artificial intelligence may strengthen learning-oriented analytics provided pedagogical relevance, transparency, data protection and evaluation of actual impacts are prioritised. Longitudinal and context-sensitive studies are needed to establish educational benefits and distributional effects.
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