education6 papersavg year 2026weak evidence

Educational activities that encourage critical AI

Research gap analysis derived from 6 education papers in our local library.

The gap

Educational activities that encourage critical AI engagement and assessment methods resilient to AI assistance need to be designed and validated in practice.

Evidence profile

Sourced from the future work and limitations of the source papers, classified as validation gap, spanning 6 journals.

Research trend

Established — well-defined area with open sub-problems.

Supporting evidence — 6 representative gaps

  • Systematic Literature Review on Students’ Perceptions of Ethical AI: Fairness, and Regulatory Understanding (2026) · Journal of Engineering Research and Education (JERE) · doi

    Educational activities that encourage critical AI engagement and assessment methods resilient to AI assistance need to be designed and validated in practice.

    validation gapfuture workevidence 5/5
    Keywords: educational activities encourage critical engagement assessment resilient assistance need designed validated practice
  • THE IMPACT OF AI IN EDUCATION (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

    Any potential AI-led education system must be moral and reliable before being put into practice so that it does not cause more harm than good, with constant testing and improvements to ensure this.

    validation gapfuture workevidence 5/5
    Keywords: potential education system must moral reliable practice cause harm good constant testing improvements ensure
  • Leveraging artificial intelligence (AI) to enhance student engagement and academic performance in higher education (2026) · Education and Information Technologies · doi

    Longitudinal investigations examining the sustained effects of AI interventions beyond a single academic year would further clarify their long-term educational impact.

    validation gapfuture workevidence 5/5
    Keywords: longitudinal investigations examining sustained effects interventions beyond single academic year further clarify long term educational
  • Use of Artificial Intelligence in Education for Smart Classrooms and Adaptive Learning Systems (2026) · International Journal for Multidimensional Research Perspectives · doi

    Continuous evaluation and research are necessary to assess the long-term impact of AI on teaching–learning processes and academic outcomes through monitoring frameworks that evaluate effectiveness of AI tools.

    validation gapfuture workevidence 5/5
    Keywords: continuous evaluation necessary assess long term impact teaching learning processes academic outcomes monitoring frameworks evaluate
  • 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

    The paper is based exclusively on secondary data from recent scholarly resources without empirical validation through implementation in real educational settings. Future research must design, develop, and experimentally validate integrated AI-based tutoring and counselling systems in actual K-12 or higher education environments to assess real-world effectiveness.

    validation gaplimitationsevidence 5/5
    Keywords: empirical validation intelligent tutoring systems real educational setting implementation experimental design
  • Perception of Use of Ai Tools Among Students of Higher Education Institutes in Mumbai (2026) · International Journal of Integrative Studies (IJIS) · doi

    The study found that 79.2% agree AI tools assist students with language barriers and learning difficulties, yet does not compare actual learning outcomes, assessment scores, or accessibility improvements between students using AI-assisted and traditional learning methods in remedial contexts.

    validation gaplimitationsevidence 5/5
    Keywords: AI tools language barriers learning difficulties accessibility student outcomes assessment

Questions about this gap

Educational activities that encourage critical AI engagement and assessment methods resilient to AI assistance need to be designed and validated in practice. This is supported by 6 representative gap statements extracted from 6 papers, rated weak evidence.

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