Hands-on Strategies for Teaching Social and Societal Impacts of Computing
Bibliographic record
Abstract
The topic of hands-on strategies for teaching the social and societal impacts of computing is of growing interest to the computer science education community because it addresses a critical gap in traditional CS curricula. While technical skills remain central, educators increasingly recognize the need to prepare students for the ethical, social, and human-centered challenges posed by modern computing technologies. From AI-driven decision-making to digital accessibility and data privacy, computing profoundly affects individuals and communities, making it essential for students to engage with these issues through experiential learning. Different viewpoints on this topic emerge based on pedagogical approaches, disciplinary perspectives, and technological optimism or skepticism. Some educators advocate for integrating service-learning and community-based projects, arguing that real-world engagement fosters empathy and ethical awareness. Others emphasize case studies and simulations, providing structured exposure to societal challenges without the unpredictability of external partnerships. Additionally, viewpoints may diverge on the role of AI: while some see AI tools as an opportunity to enhance social good, others worry they may exacerbate biases and reduce human agency in computing. Despite these differences, there is broad agreement that computing education must go beyond technical training to include a deeper understanding of computing's role in society. To bridge this big-picture overview with practical action, the panel will discuss six complementary, hands-on strategies ranging from service-learning studios and virtue-anchored 3Cs project lessons to indigenous game modules, intergenerational tech pop-ups, humanitarian FOSS work, and ethics-infused case-study frameworks. Together, they offer diverse yet transferable ways to tackle the shared challenge of embedding social impact into computing coursework.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.061 | 0.014 |
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".