The study identifies that AI tools showing low

The gap

Evidence profile

Sourced from the future work of the source papers, classified as methodology gap, spanning 3 journals.

Supporting evidence — 3 representative gaps

• Students’ perceptions and responsible adoption of artificial intelligence in education: Ethical considerations, impacts, and academic performance (2026) · International Journal of Applied Resilience and Sustainability · doi

  The study identifies that AI tools showing low transparency in outputs (43% disagreement) is a significant student concern, but deeper investigation into explainability mechanisms and their effectiveness is needed.

  methodology gapfuture workevidence 5/5
  Keywords: identifies tools showing transparency outputs disagreement significant student concern deeper investigation explainability mechanisms effectiveness needed
• Artificial Intelligence Adoption in Education Opportunities Challenges and Future Directions (2026) · International Journal for Multidimensional Research Perspectives · doi

  The paper identifies algorithmic bias and lack of transparency in AI decision-making as significant ethical concerns but does not specify empirical studies that quantify bias rates in educational AI systems or benchmark explainable AI implementations in actual classroom settings. Research directly measuring fairness outcomes and algorithmic transparency across different student populations in personalized learning environments is needed.

  methodology gapfuture workevidence 5/5
  Keywords: algorithmic bias explainable AI fairness transparency educational AI systems personalized learning
• Intelligent Tutoring and Counselling Systems in Education: A Comprehensive Review of AI- Driven Personalized Learning and Career Guidance. (2026) · International Journal For Multidisciplinary Research · doi

  Explainable AI models remain absent from current intelligent tutoring and counselling systems, hindering transparency and user trust. Future development must create interpretable machine learning models that make AI decision-making processes transparent and accountable to educators, students, and stakeholders.

  methodology gapfuture workevidence 5/5
  Keywords: explainable AI transparent machine learning interpretability intelligent tutoring systems accountability

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