Confirmation bias and trust: Human factors that influence teachers' attitudes towards AI-based educational technology

Abstract

Evidence from various domains underlines the key role that human factors, and especially, trust, play in the adoption of AI-based technology by professionals. As AI-based educational technology is increasingly entering K-12 education, it is expected that issues of trust would influence the acceptance of such technology by educators as well, but little is known about this matter. In this work, we bring the opinions and attitudes of science teachers that interacted with several types of AI-based technology for K-12. Among other things, our findings indicate that teachers are reluctant to accept AI-based recommendations when it contradicts their previous knowledge about their students and that they expect AI to be absolutely correct even in situations that absolute truth may not exist (e.g., grading open-ended questions). The purpose of this paper is to provide initial findings and start mapping the terrain of this aspect of teacher-AI interaction, which is critical for the wide and effective deployment of AIED technologies in K-12 education.

An Instrument for Measuring Teachers’ Trust in AI-Based Educational Technology

“…The theory of academic resistance [41] treats the general issue of technology resistance in educational settings, the resistance to organizational change theory [37] proposes a multidimensional view on resistance to change in general and suggests that individuals operate in three dimensions (cognitive, affective, and behavioral) and simultaneously may be ambivalent in all three dimensions, and technology acceptance model (TAM) [14] explains the perceived usefulness of technology and its usage intentions in terms of social influence and cognitive instrumental processes. However, none of the above theories focuses on the 'AI nature' of AI-based tools, which, based on evidence from other disciplines [8,23] and our preliminary research [35], plays a significant and even critical role in forming practitioners' perceptions and attitudes towards adoption AI-based EdTech.…”

“…The themes that emerged were discussed with the other authors based on the data and triangulated between the interviews and the group discussions. As a result of this qualitative phase [35], a dozen items were added to the instrument (Q1-Q6, Q16-Q20, and Q23; See the instrument in Appendix A.1).…”

“…• Item Q13 is related to the lack of transparency in how AI makes decisions. We decided to retain this item in the instrument based on our preliminary research with teachers [35], where the issues of lack of general understanding of how machine learning works and what can and cannot be expected from AI-based system were detected as substantial reasons for not trusting AI-based insights. We called this factor "AI-based EdTech's Perceived Lack of Transparency".…”

“…• Item Q20 deals with an issue of significant change in ways teachers teach today due to potential AI-based EdTech usage. Based on our previous research [35], we decided to retain this item in the Instrument, as this subject was raised frequently in our interviews with teachers and teacher group discussions related to their experiences with recently developed AI-based EdTech tools (presented in [35]) in their classrooms. We called this factor "Required Shift in Pedagogy to Adopt AI-based EdTech".…”

Teachers' trust in AI‐powered educational technology and a professional development program to improve it

“…Such possible differences between AI‐EdTech suggestions and teachers' opinions are likely to reduce their trust in AI‐EdTech (Nazaretsky et al, 2022). These phenomena might be attributed to the manifestation of fundamental human bias and heuristics of judgement under uncertainty (Tversky & Kahneman, 1974), such as Confirmation Bias and Belief Perseverance (Nickerson, 1998), and they can further lead teachers' resistance to the adoption of AI‐EdTech recommendations (Nazaretsky et al, 2021).…”