[Paper Review] Charting the Future of AI in Project-Based Learning: A Co-Design Exploration with Students

Chengbo Zheng, Kangyu Yuan+5 more|arXiv (Cornell University)|Jan 26, 2024

Biomedical and Engineering Education6 citations

TL;DR

The paper presents a qualitative co-design study with 18 college students to explore how AI usage data in project-based learning could serve as a novel material for PBL assessment and reporting.

ABSTRACT

The increasing use of Artificial Intelligence (AI) by students in learning presents new challenges for assessing their learning outcomes in project-based learning (PBL). This paper introduces a co-design study to explore the potential of students' AI usage data as a novel material for PBL assessment. We conducted workshops with 18 college students, encouraging them to speculate an alternative world where they could freely employ AI in PBL while needing to report this process to assess their skills and contributions. Our workshops yielded various scenarios of students' use of AI in PBL and ways of analyzing these uses grounded by students' vision of education goal transformation. We also found students with different attitudes toward AI exhibited distinct preferences in how to analyze and understand the use of AI. Based on these findings, we discuss future research opportunities on student-AI interactions and understanding AI-enhanced learning.

Motivation & Objective

• Motivate how AI usage in PBL challenges traditional assessment of learning outcomes.
• Investigate how students might report their AI-assisted learning processes for fair assessment.
• Explore future education goals and traits needed in AI-rich PBL through student-driven design.
• Identify potential methods to analyze and report AI usage to support learning outcome evaluation.

Proposed method

• Conducted seven three-hour co-design workshops with 18 college students (qualitative study).
• Used activities: AI-involved PBL journey speculation, imagine the ideal student, and AI usage report design.
• Applied ground-in theory triangulation (source, investigator, theory) to validate findings.
• Collected data from learner journeys, AI capability cards, and student-designed AI usage reports.
• Performed thematic analysis to derive future AI usage scenarios and reporting approaches.
• Discussed ethical and practical considerations of transparency and interpretation in human-AI interactions.

Experimental results

Research questions

• RQ1 How might students imagine and document AI usage in PBL to transparently communicate their learning processes?
• RQ2 What AI usage scenarios and learning-goal transformations do students foresee for AI-enabled PBL?
• RQ3 What traits of an AI-enabled student are envisioned, and how could these traits be assessed fairly?
• RQ4 What data should be included in an AI usage report to support assessment of higher-order learning outcomes?

Key findings

• Participants generated a broad set of existing and envisioned AI usage scenarios in PBL grounded in their past experiences.
• Students proposed multiple methods for analyzing interactions with AI to yield insights for evaluating learning outcomes.
• Post-workshop interviews showed that different attitudes toward AI led to distinct preferences for representing human-AI interaction in reports.
• Participants highlighted concerns about ambiguous interpretation and fairness in evaluating human-AI interactions.
• The study provides groundwork for future research on student–AI interaction, reporting, and sensemaking in AI-enhanced learning.