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

TL;DR

This study addresses how to assess AI-enhanced learning in project-based learning by exploring students' AI usage data as an assessment material. It uses seven 3-hour co-design workshops with 18 college students to elicit future AI usage scenarios, envisioned assessment transformations, and reporting designs, analyzed via inductive thematic analysis and member checks. Key contributions include mappings of AI usage categories, traits for future students, and diverse reporting schemes that link AI interactions to learning outcomes, along with a set of research opportunities on student-AI interaction and tracking/sensemaking. The findings highlight varied beliefs about AI’s role (tool, teammate, or expert) and emphasize designing transparent, fair, and reflective AI-augmented assessment practices with attention to SRL and collaboration dynamics.

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.

Figures (3)

• Figure 1: Our co-design workshop involved three activities (AC). In AC1, students mapped their learning journey (1) and envisioned AI integration in projects with the help of AI Capability Cards (2). AC2 involved identifying key traits for student assessment in a PBL context (3), AI's impact on these assessments (4), and emergent traits necessary in an AI-rich future (5). In the last activity (AC3), based on the outcomes of the prior two activities, students first considered what data should be covered in a report on their AI usage in PBL (6) and then visually designed the report (6).
• Figure 2: The workshop utilized the Miro platform for collaborative activities. The facilitator shared the Miro board screen throughout (1). In the first activity, students described their project journey in our provided learning journey table (1). The second activity involved identifying current and future PBL traits using colored sticky notes—orange for current PBL importance, purple for future needs, and orange with ticks for traits affected by AI's rise (2). In the third activity, students linked traits from the second activity to AI usage, using sticky notes for detailed notes (3(a)). For the visual report, they could use Miro's tools for design mockups (e.g., P16), incorporate external materials (e.g., P02), or sketch using preferred tools (3(b)).
• Figure 3: A subset of designs of the reporting of students' AI usage resulted from step 2 of activity 3 of the co-design workshop. We explain the designs when we mention them in the main text.