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AI evidence extraction

Transforming or Undermining Learning using GenAI: A Systematic Literature Review and Design Science Study

William Tam, Khushbu Tilvawala, David Sundaram · Journal of the Association for Information Systems · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

7/10
Relevance
0/4
Quality (LMQS)
D
Evidence
0
Citations
0.00
FWCI

Methodology & findings

Study design

Design science research method combining systematic literature review for observation, systems thinking approach for theorizing conceptual artifacts, design of system artifacts using prompt engineering and simulations, and evaluation through interviews and literature review..

Primary method

Design science research method combined with systems thinking approach

Main result

The study found that "the key conceptual and systems artifacts from this study would be of value to university students, recent graduates, educators, and employers, enabling them to mitigate the dark side and enhance the bright side of GenAI-based learning." The research explored how GenAI tools can "sustain and positively transform their learning" while addressing both beneficial and harmful aspects of technology integration.

Research paradigm

Design science research with systems thinking approach

Author conclusions

The authors conclude that their research "contributes to GenAI and learning research by offering a novel, holistic lens that attempts to support sustained, adaptive, lifelong, and unsupervised learning" and that the developed artifacts "would be of value to university students, recent graduates, educators, and employers, enabling them to mitigate the dark side and enhance the bright side of GenAI-based learning."

Risk of bias

Selection bias in literature review (search strategy and inclusion/exclusion criteria not detailed in abstract); Limited empirical validation of artifacts (evaluation through interviews and literature review only, not yet through rigorous empirical studies); Potential confirmation bias in systems thinking approach for theorizing artifacts; No control group or comparative evaluation mentioned

Limitations

  • The authors acknowledge that "Future work will involve validation of the proposed artifacts through in-depth empirical studies," indicating that the current study lacks comprehensive empirical validation of the designed artifacts.

Open questions raised

  • The authors identify the need for validation of the proposed artifacts through in-depth empirical studies as a future research direction.
  • The authors identify the need for validation of proposed artifacts through in-depth empirical studies in future work. They also highlight the gap between exploratory design and rigorous empirical evaluation of GenAI-based learning tools.
  • Future work will involve validation of the proposed artifacts through in-depth empirical studies to strengthen the evidence base for GenAI-based learning interventions.
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