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Scaffolds, screens, and readiness: How teacher-guided AI lifts special-education students into conversational agents
Lan Liu1, Wen Liu2, Ziwei Luo3
1School of Teacher Education, Suqian University, Suqian, Jiangsu, China; Department of Education Management, China-ASEAN International College, Dhurakij Pundit University, Bangkok, Thailand.
Abstract:
This research examination focuses on the role of teacher AI scaffolding, the supportive guidance teachers give regarding the use of AI tools, on the conversational AI-readiness of students in special education. We administered a research survey to 562 undergraduates (convenient sampling) with disabilities across five Chinese provinces and tested a moderated-mediation model based on Self-determination theory (SDT) using Smart_PLS. Findings show that teacher AI scaffolding exerts a small but significant direct impact on the readiness of students to use academic Chatbots (beta = 0.12, p < .01). The majority of its effects work through two mediators: perceived Chatbot pedagogical intelligence (a competence-related perception; indirect 95 % confidence interval-CI 0.08 to 0.12) and digital self-regulated learning (an autonomy-related behavior; indirect 95 % CI 0.10 to 0.16), both with 95 % CIs (non-zero). Additionally, the strength of these indirect pathways is considerably heightened by increased EdTech media immersion (students' exposure to educational technology), whereby the route via competence (interaction effect 0 0.03, p < .01) and the route via self-regulation (interaction effect 0 0.06, p < .05) increase with higher media immersion. These results are consistent with SDT, which suggests that teacher support facilitates the fulfillment of the basic needs that lead to the adoption of technology. To increase the acceptance of Chatbots by students with special needs, we encourage the use of inclusive education programs that use teacher-guided AI scaffolding. The educational policy ought to be based on training teachers in AI-enhanced learning and student data protection, following the latest national recommendations on AI in education in China. This study contributes to theory by integrating motivational mediators and a media context moderator to explain AI readiness (49 % variance explained), and offers practical guidance for leveraging AI in special education.
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