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Related Experiment Video

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Using explainable AI to align pre-university profiles with bachelor's degree success.

Juan Ramón Rico-Juan1, María Luisa Pertegal-Felices2, Antonio Jimeno-Morenilla3

  • 1Department of Software and Computing Systems, University of Alicante, Alicante, Spain.

Scientific Reports
|December 23, 2025
PubMed
Summary

A new machine learning tool helps students choose compatible bachelor's degrees, improving academic success. By analyzing pre-university data, the degree recommendation system predicts suitable programs, reducing dropout rates.

Keywords:
Academic performanceCareer counselingExplainable artificial intelligence (XAI)Machine learning (ML)Recommender systemStudent dropoutVocational orientation

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Area of Science:

  • Educational Technology
  • Computer Science
  • Data Science

Background:

  • Student transition to higher education is often hindered by a lack of information on academic program compatibility.
  • Poor course selection can lead to decreased academic performance and increased dropout rates.
  • Existing guidance methods may not fully leverage student academic profiles for optimal degree selection.

Purpose of the Study:

  • To develop a machine learning (ML)-based degree recommendation tool for aligning pre-university profiles with bachelor's degree programs.
  • To provide data-driven guidance for students and assist school counselors in career advising.
  • To reduce academic underperformance and dropout rates by enhancing course selection accuracy.

Main Methods:

  • Utilized machine learning algorithms on a large dataset of academic records (approx. 72,000) from a Spanish university (2010-2022).
  • Developed a degree recommendation system incorporating student academic data, interests, and socioeconomic factors.
  • Employed explainability techniques to identify student profiles and subject-degree relationships.

Main Results:

  • The recommendation tool achieved an average accuracy of 70% for the top 5 predictions and 90% for the top 10.
  • Identified specific pre-university subjects (e.g., Geography, Mathematics, Physics) influencing degree recommendations.
  • Revealed correlations between academic backgrounds and suitability for various bachelor's degree programs.

Conclusions:

  • The ML-powered degree recommendation tool offers accurate and personalized guidance to students.
  • The system can significantly aid educational institutions in improving student academic outcomes and retention.
  • Future enhancements include expanding data sources and incorporating advanced ML models for greater precision.