New research reveals that student acceptance of AI grading depends on six key factors beyond traditional technology adoption models. This session presents the validated TAM-AIG framework, showing how perceived fairness, transparency, and human oversight drive student trust—with surprising findings about what students value most in automated assessment systems.

As AI grading systems become widespread in education, understanding student acceptance has become critical for successful implementation. While much attention focuses on technical capabilities, less research examines the psychological factors that determine whether students will trust these technologies. 

Traditional technology acceptance models fall short because they don't account for unique concerns about algorithmic judgment of academic work. Issues of fairness, transparency, and human oversight become paramount when AI evaluates student learning. 

This session presents research that developed and validated the Technology Acceptance Model for AI Grading (TAM-AIG), revealing the relationships between six key constructs that drive student attitudes. The findings challenge assumptions about what students value and show that acceptance depends on how systems are designed, implemented, and communicated. 

Interactive Engagement Plan 

Throughout the session, participants will engage with key questions that emerged from the research: 

• Poll Question: "What concerns you most about AI grading: accuracy, fairness, or loss of human connection?" (followed by research findings on actual student priorities) 
• Discussion Prompt: "How might student attitudes toward AI grading differ across disciplines?" (small group discussions with report-back) 
• Q&A Integration: Questions will be woven throughout rather than saved for the end, allowing for dynamic interaction with the research findings 

Session Structure and Takeaways 

Opening (10 minutes) 

• The AI grading acceptance challenge 
• Why traditional technology models don't apply 
• Overview of TAM-AIG research methodology 

Core Findings (20 minutes) 

• Presentation of seven hypotheses and results 
• Surprising findings that challenge assumptions 
• The validated TAM-AIG model and construct relationships 

Practical Applications (10 minutes) 

• Implementation strategies based on research findings 
• Communication frameworks for introducing AI grading 
• Design principles for acceptable AI grading systems 

Interactive Planning (5 minutes) 

• Participant application of TAM-AIG to their contexts 
• Quick sharing of implementation insights 

Attendee Takeaways 

Participants will gain: 

Theoretical Understanding

• Comprehensive knowledge of the TAM-AIG framework and its six constructs 
• Understanding of the specific relationships between fairness, transparency, trust, and acceptance 

Practical Tools 

• Communication strategies for introducing AI grading to students
• Design principles for AI grading systems that students will accept 

Research-Based Insights 

• Counterintuitive findings about what students actually value in AI grading
• Data-driven understanding of student concerns and priorities 

Strategic Perspective 

• Framework for predicting student acceptance of new AI grading initiatives
• Understanding of how to build trust and transparency in AI assessment systems
• Insights into the role of human oversight in AI-augmented education 

This session provides attendees with both theoretical understanding and practical tools for successfully implementing AI grading systems that students will trust and accept. By grounding recommendations in rigorous empirical research, participants gain confidence in making evidence-based decisions about AI integration in their educational contexts. 

Hayden Noel

Clinical Associate Professor at University of Illinois Urbana-Champaign

Adam King

Sr Assoc Director of T&L Technology at University of Illinois Urbana-Champaign

Adam King is the Sr Assoc Director of Teaching & Learning Technology at the University of Illinois Urbana-Champaign Gies College of Business. Adam has 20 years of experience in business education and entrepreneurial settings. He joined Gies in 2017, working in instructional design and learner support. Today, Adam leads efforts to leverage new technologies that further the success of Gies learners, faculty, and staff. Gies is unique in its at-scale approach to high-quality affordable education.

Julia Sabin

Consultant at University of Illinois Urbana-Champaign