[Paper Review] Who is Helping Whom? Student Concerns about AI- Teacher Collaboration in Higher Education Classrooms

Bingyi Han, Simon Coghlan+2 more|arXiv (Cornell University)|Dec 19, 2024

Online Learning and Analytics4 citations

TL;DR

This study investigates student perceptions of AI-teacher collaboration in higher education using story completion with 65 participants, revealing three core challenges: AI's decontextualization of learning, biased teacher-AI cooperation, and behavioral changes in students that undermine AI effectiveness. The paper advocates for context-sensitive AIEd design that accounts for stakeholder dynamics, power imbalances, and ethical implications in real-world educational settings.

ABSTRACT

AI's integration into education promises to equip teachers with data-driven insights and intervene in student learning. Despite the intended advancements, there is a lack of understanding of interactions and emerging dynamics in classrooms where various stakeholders including teachers, students, and AI, collaborate. This paper aims to understand how students perceive the implications of AI in Education in terms of classroom collaborative dynamics, especially AI used to observe students and notify teachers to provide targeted help. Using the story completion method, we analyzed narratives from 65 participants, highlighting three challenges: AI decontextualizing of the educational context; AI-teacher cooperation with bias concerns and power disparities; and AI's impact on student behavior that further challenges AI's effectiveness. We argue that for effective and ethical AI-facilitated cooperative education, future AIEd design must factor in the situated nature of implementation. Designers must consider the broader nuances of the education context, impacts on multiple stakeholders, dynamics involving these stakeholders, and the interplay among potential consequences for AI systems and stakeholders. It is crucial to understand the values in the situated context, the capacity and limitations of both AI and humans for effective cooperation, and any implications to the relevant ecosystem.

Motivation & Objective

• To understand how students perceive AI-teacher collaboration in higher education classrooms.
• To identify emerging challenges in AI-facilitated educational cooperation from the student perspective.
• To examine the implications of AI observing students and alerting teachers for targeted intervention.
• To highlight ethical and practical concerns related to power dynamics, bias, and contextual understanding in AI-aided education.
• To inform future AIEd design by emphasizing the situated nature of educational contexts and stakeholder interplay.

Proposed method

• Applied the story completion method to elicit narratives from 65 higher education students on AI-teacher collaboration scenarios.
• Collected and analyzed qualitative narratives to identify recurring themes and concerns about AI integration.
• Focused on scenarios involving AI observing students and notifying teachers for intervention.
• Thematic analysis was used to categorize student concerns into three core challenges: decontextualization, bias in cooperation, and behavioral impacts.
• Explored the interplay between AI, teachers, and students in real classroom dynamics.
• Used findings to argue for context-aware AIEd design that respects educational nuance and stakeholder roles.

Experimental results

Research questions

• RQ1 How do students perceive the role of AI in observing and supporting them within teacher-AI collaborative learning environments?
• RQ2 What concerns do students express regarding power imbalances and bias in AI-teacher partnerships?
• RQ3 How does AI's presence influence student behavior, and what are the implications for AI's effectiveness?
• RQ4 In what ways does AI decontextualize or misrepresent the educational context in student-teacher interactions?
• RQ5 How can AIEd systems be designed to respect the situated, dynamic, and ethical realities of higher education classrooms?

Key findings

• Students expressed significant concern that AI decontextualizes classroom interactions, reducing complex learning situations to data points without understanding context.
• Students reported fears of biased teacher-AI collaboration, particularly when AI alerts lead to unequal treatment or disciplinary actions.
• Students noted that awareness of AI monitoring led to self-censorship and altered behavior, undermining authentic learning and reducing AI's ability to detect genuine learning needs.
• The study found that power disparities between teachers and students are exacerbated when AI mediates teacher interventions, raising equity and trust issues.
• Students emphasized that AI systems often fail to account for individual differences, emotional states, and situational nuances in learning contexts.
• The research concludes that effective AIEd requires design that integrates the full educational ecosystem, including values, limitations, and stakeholder dynamics.