Developing AI Literacy Competencies Among First-Year Engineering Students
Bibliographic record
Abstract
As artificial intelligence (AI) continues to become a significant component of modern engineering, it is important that incoming students learn foundational AI concepts to prepare for related challenges and opportunities in their careers. This study proposes the creation of research-informed online training for incoming engineering students to build their competencies in AI literacy and effective and responsible use of AI. Educational personas were created to better understand the experiences of incoming student. These personas ranged from students with no prior exposure to AI to those with an in-depth understanding of generative AI and large language models. Eight distinct units were established, each accompanied by targeted learning objectives and informed by several factors: gaps identified in the personas, insights from the literature reviews, and the specific skill deficiencies highlighted by the rubric. An online learning module was created in RISE, an online course development platform that supports interactive and engaging learning experiences. Designed activities included sorting exercises and matching definitions, which helped reinforce key concepts. Pilot testing of the final module was performed in an introductory coding course for first-year engineering students. Preliminary feedback from students and teaching team members has been highly positive, highlighting the potential for broader applicability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".