# Understanding Human-AI Trust in Education

arXiv: https://arxiv.org/abs/2506.09160 · HTML: https://mlsanity.com/paper/2506.09160/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 132 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [Automatic Generation of Programming Exercises and Code Explanations using Large Language Models](https://mlsanity.com/paper/2206.11861/) (2022)
- [The relationship between trust in AI and trustworthy machine learning technologies](https://mlsanity.com/paper/1912.00782/) (2019)
- ["It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers](https://mlsanity.com/paper/2304.02491/) (2023)
- [Trust in AI: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2403.14680/) (2024)
- [A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective](https://mlsanity.com/paper/2304.08795/) (2023)
- [Trust in AI and Its Role in the Acceptance of AI Technologies](https://mlsanity.com/paper/2203.12687/) (2022)
- [Generative AI in Higher Education: A Global Perspective of Institutional Adoption Policies and Guidelines](https://mlsanity.com/paper/2405.11800/) (2024)
- [Generative Artificial Intelligence in Higher Education: Evidence from an Analysis of Institutional Policies and Guidelines](https://mlsanity.com/paper/2402.01659/) (2024)
- [Generative AI in Higher Education: Seeing ChatGPT Through Universities' Policies, Resources, and Guidelines](https://mlsanity.com/paper/2312.05235/) (2023)
- [Student Perspectives on the Benefits and Risks of AI in Education](https://mlsanity.com/paper/2505.02198/) (2025)
- [Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-methods Study in the U.S. Higher Education](https://mlsanity.com/paper/2502.05770/) (2025)

## Built on by (most cited first, 4 of 4)

- [Students' Reliance on AI in Higher Education: Identifying Contributing Factors](https://mlsanity.com/paper/2506.13845/) (2025)
- [Trust and Reliance on AI in Education: AI Literacy and Need for Cognition as Moderators](https://mlsanity.com/paper/2604.01114/) (2026)
- [What Drives Students' Use of AI Chatbots? Technology Acceptance in Conversational AI](https://mlsanity.com/paper/2602.20547/) (2026)
- [Automated Recommendation of Programming Learning Content Using Pattern-based Knowledge Components](https://mlsanity.com/paper/2607.05409/) (2026)

## Abstract (arXiv)

As AI chatbots become integrated in education, students are turning to these systems for guidance, feedback, and information. However, the anthropomorphic characteristics of these chatbots create ambiguity over whether students develop trust in them in ways similar to trusting a human peer or instructor (human-like trust, often linked to interpersonal trust models) or in ways similar to trusting a conventional technology (system-like trust, often linked to technology trust models). This ambiguity presents theoretical challenges, as interpersonal trust models may inappropriately ascribe human intentionality and morality to AI, while technology trust models were developed for non-social systems, leaving their applicability to conversational, human-like agents unclear. To address this gap, we examine how these two forms of trust, human-like and system-like, comparatively influence students' perceptions of an AI chatbot, specifically perceived enjoyment, trusting intention, behavioral intention to use, and perceived usefulness. Using partial least squares structural equation modeling, we found that both forms of trust significantly influenced student perceptions, though with varied effects. Human-like trust was the stronger predictor of trusting intention, whereas system-like trust more strongly influenced behavioral intention and perceived usefulness; both had similar effects on perceived enjoyment. The results suggest that interactions with AI chatbots give rise to a distinct form of trust, human-AI trust, that differs from human-human and human-technology models, highlighting the need for new theoretical frameworks in this domain. In addition, the study offers practical insights for fostering appropriately calibrated trust, which is critical for the effective adoption and pedagogical impact of AI in education.