AI evidence extraction

Bridging the Skills Gap: A Course Model for Modern Generative AI Education

Anya Bardach, Hamilton Murrah · Proceedings of the AAAI Conference on Artificial Intelligence · 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)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1609/aaai.v40i47.41502

Methodology & findings

Study design

Mixed method surveys with data analysis and reflections from both instructor and student perspectives.

Main result

The study found that "Two mixed method surveys indicated students overwhelmingly found the course valuable and effective." The research demonstrates that students across Computer Science trajectories recognized the importance of formal instruction in generative AI applications, with the course serving as a bridge between industry demands for AI competency and traditional academic Computer Science education.

Reports effect sizes.

Research paradigm

Mixed methods (qualitative and quantitative)

Author conclusions

The authors argue that "students across fields must be taught to responsibly and expertly harness the potential of AI tools to ensure job market readiness and positive outcomes." They further assert this is particularly urgent for Computer Science, noting the disconnect between industry demand for generative AI competency and higher education's limited course offerings on practical applications of existing generative AI tools.

Risk of bias

Selection bias: Limited to single institution (private research university); Attrition/Response bias: Not specified in abstract; Observer bias: Co-authored by course instructor and graduate student (potential confirmation bias); Generalizability concerns: Single course implementation may not represent broader CS departments; Selection bias: Participants were self-selected students enrolled in the course; Response bias: Students surveyed may have been those more satisfied with the course; Instructor bias: Co-authorship by the course instructor and a graduate student creates potential bias in reporting outcomes; Selection bias: Students self-selected into the course; Response bias: Survey respondents may have been more satisfied students; Instructor bias: Course taught by authors who also evaluated it; Limited generalizability: Single institution context

Open questions raised

The paper identifies that while many top-ranked Computer Science departments teach underlying AI mechanisms and frameworks, "few have started offering courses on applications for existing generative AI tools." The authors additionally offer recommendations for replication in and beyond Computer Science departments, suggesting an identified need for broader curriculum development.
The authors identify that while top-ranked Computer Science departments teach underlying mechanisms and frameworks of AI, "few have started offering courses on applications for existing generative AI tools." This gap between industry demand and educational supply motivates the course development.
The paper identifies that while many top-ranked Computer Science departments teach the mechanisms and frameworks underlying AI, "few have started offering courses on applications for existing generative AI tools," and addresses a disconnect between industry demand for generative AI competency and its absence in higher education curricula.

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