GenAI is reshaping assessment practices in computer science education through automation, scalability, and personalized feedback¶

Claim · All claims
Evidence · 1 study · theoretical r? · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? GenAI is profoundly reshaping CS assessment practices by enabling automation, scalability, and personalized feedback mechanisms. → Zhan 2026

Evidence¶

Zhan 2026¶

Zhan, S., & Chapman, E. (2026). Harnessing Generative Artificial Intelligence in Computer Science Education: Pedagogical Innovation, Ethical Responsibility, and the Future of Assessment. Journal of University Teaching and Learning Practice. https://doi.org/10.53761/wakxak53

q2 · i? · theoretical · r?

The paper's opening synthesis, drawing on contemporary literature and emerging use cases, asserts that "Generative AI (GenAI), is profoundly reshaping assessment practices in computer science education" via automation, scalability, and personalized feedback. No empirical effect size is reported; this is a narrative-review-level assertion.

"Artificial Intelligence (AI), particularly Generative AI (GenAI), is profoundly reshaping assessment practices in computer science education by enabling automation, scalability, and personalized feedback mechanisms."

Discussion¶

Related Claims¶

• GenAI in CS education is argued to enhance student engagement, support equitable learning, and relieve instructional burden — related
• AI integration is associated with improvements in teaching, learning, and administrative efficiency in higher education when supported by adequate infrastructure — a broader claim this one bears on
• GenAI offers transformative possibilities for personalised learning, research support and administrative efficiency in Global South higher education — a broader claim this one bears on
• Reviewed AI literature portrays AI as having transformative capacity to reshape educational methodologies, with a need for balancing guidelines — a broader claim this one bears on
• Reviewed literature identifies personalisation, adaptive support, rapid feedback, and teacher-facing automation as recurring pedagogical opportunities of AI in mathematics education — related
• AI presents four solution types for overcoming situated learning barriers: adaptive systems, intelligent tutoring in authentic scenarios, administrative automation, and data-driven teacher support — related
• LLMs can deliver scalable, personalized, interactive feedback almost instantly, reducing dependency on teacher availability and supporting self-regulated learning in large courses — a narrower finding that bears on this claim
• GAI in K-12 education offers opportunities to personalize learning, motivate students, improve assessment, and introduce innovative teaching practices — related
• Effectiveness of AI-driven personalized learning is context-dependent, with CS and Mathematics students showing contrasting engagement and performance trajectories — related
• Staff recognise substantial opportunities to use GenAI to enhance teaching through streamlined administrative processes, improved curriculum and assessment design, and student-centred learning activities. — related