Imagining Computing Education Assessment after Generative AI
Project Overview
The document examines the transformative influence of generative AI on education, particularly in computing, where it poses significant challenges to traditional assessment methods by enabling students to generate code and answer questions autonomously, thereby threatening academic integrity. In response to these challenges, the authors advocate for a shift towards 'ungrading,' a pedagogical approach that prioritizes intrinsic motivation and student-centered learning over conventional grading systems. This transition is presented as a necessary adaptation to the realities of generative AI, suggesting that it can enhance learning experiences by fostering deeper engagement and understanding among students. The findings indicate that embracing these innovative assessment practices can not only mitigate the risks associated with AI but also enrich the educational landscape, ultimately leading to more meaningful and effective learning outcomes.
Key Applications
Generative AI tools for assessment and feedback
Context: Higher education settings, targeting both educators and students, including computing courses and broader educational assessment methods.
Implementation: Integration of generative AI tools into coursework and assessments, including the adoption of ungrading practices and flexible assessment methodologies that focus on engaging students in intrinsic learning rather than relying solely on traditional grading.
Outcomes: ['Potential for improved learning through personalized feedback instead of grades.', 'Increased student motivation and engagement.', 'Focus on intrinsic learning rather than extrinsic grades.']
Challenges: ['Concerns about academic integrity regarding the use of AI in coding and assessments.', 'Resistance from educators towards new assessment methodologies.', 'Complexity in implementing new assessment strategies.']
Implementation Barriers
Cultural/Institutional
Resistance to change from traditional grading systems to ungrading methods
Proposed Solutions: Fostering dialogue among educators about the benefits of ungrading; providing training on new assessment methods
Technical/Practical
Challenges in maintaining academic integrity with the use of generative AI tools
Proposed Solutions: Developing proctored assessments; creating assessments that are less reliant on traditional methods
Project Team
Stephen MacNeil
Researcher
Scott Spurlock
Researcher
Ian Applebaum
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Stephen MacNeil, Scott Spurlock, Ian Applebaum
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai