Enterprise AI Analysis
Beyond the Benefits: A Systematic Review of the Harms and Consequences of Generative Al in Computing Education
Authors: Seth Bernstein, Ashfin Rahman, Nadia Sharifi, Ariunjargal Terbish, Stephen MacNeil
A systematic literature review (SLR) focusing on GenAI's harms in computing education found 224 relevant papers (2022-2025). The review identified six broad categories of harm: cognitive, metacognitive, assessment and academic integrity, equity, social, and instructional/logistical. Cognitive and metacognitive harms were most prevalent, while social harms were least frequently discussed. Many harms still lack strong empirical evidence, often being based on speculation rather than measurement. The study highlights methodological gaps and under-explored harms (e.g., social interactions, collaboration). It aims to provide educators, students, researchers, and developers with a clear picture of GenAI's negative impacts to enable effective mitigation.
Key Findings from the Research
This systematic review consolidates critical data points, offering a clear perspective on the landscape of GenAI's impact in computing education.
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Papers Reviewed
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Harms Discussed (2024)
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Cognitive Harms (Most Prevalent)
Deep Analysis & Enterprise Applications
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Systematic Literature Review Process
Identification
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Screening
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Included
| Harm Category | Definition | Key Findings (Paper Analysis) |
|---|---|---|
| Cognitive | Impacts how students acquire/apply knowledge. |
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| Metacognitive | Disrupts planning, monitoring, and adapting problem-solving strategies; over-reliance and trust calibration issues. |
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| Assessment & Academic Integrity | Includes AI-enabled cheating, plagiarism, and fairness/enforcement issues. |
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| Equity | Model biases (language, race, gender) and unequal access/benefits. |
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| Social Harms | Reduces collaboration, increases isolation, affects peer interactions. |
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| Instructional & Logistical | Challenges for instructors: data privacy, new competencies, changing relevance of skills, scaffolding AI use. |
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Impact of GenAI on Learning Communities
A recent study by Hou et al. [93] showed negative impacts on peer interactions and learning communities. Students reported relying on ChatGPT to avoid socio-emotional barriers of help-seeking, stating it's 'easier for me to ask ChatGPT because I don't have to worry... I feel like it is kind of rude to continuously ask for more and more details.' Arora et al. [20] found that LLMs hindered collaboration due to compatibility issues between LLM-generated code from different team members.
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Papers with Measured Evidence for Harms (2024)
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Your AI Implementation Roadmap
A strategic phased approach to integrate GenAI while mitigating risks and maximizing benefits in computing education.
Phase 1: AI Readiness Assessment
Evaluate current computing education curricula, instructor preparedness, and student AI literacy levels. Identify key areas where GenAI tools are currently being used and where harms are emerging.
Phase 2: Policy & Ethical Framework Development
Establish clear guidelines for GenAI use in assignments, assessments, and learning activities. Develop ethical frameworks addressing academic integrity, data privacy, and fairness. Communicate policies effectively to students and staff.
Phase 3: Pedagogical Re-design & Scaffolding
Revise assignments to be 'AI-resistant' or 'AI-integrated' focusing on higher-order thinking, problem specification, and critical evaluation of AI outputs. Implement scaffolding strategies to guide responsible and effective AI tool use.
Phase 4: Instructor Training & Support
Provide professional development for educators on integrating AI tools, detecting misuse, and fostering critical AI literacy. Support instructors in adapting their teaching practices to address new challenges and opportunities.
Phase 5: Continuous Monitoring & Research
Regularly collect data on student AI usage, learning outcomes, and perceptions of harms. Conduct empirical studies to build evidence-based practices and inform ongoing adjustments to policies and pedagogies.
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