Effect of Artificial Intelligence (AI)-Scaffolded Planning Tools on Research Question Formulation Among Students with Mild Intellectual Disabilities: A Randomized Controlled Trial Analysis - Ofem, Usani Joseph; Anake, Pauline; Anyin, Nnyenkpa Ntui; Nsan, Nsan; Okon, Maureen Okang; Abuo, Cyril Bisong; Olonye, Stephen; Adie, Joy; Obibessong, Vera Mpuon; Ukatu, James Omaji; Ogar, Agede Ambor; Ebaye, Asenath Sylvester; et al. (Journal of Intellectual Disability - Diagnosis and Treatment, 2025)
Introduction: Students with mild intellectual disabilities often encounter significant difficulties when faced with complex academic tasks, particularly in developing research topics. These challenges are not only linked to their cognitive limitations but are also compounded by limited instructional support within learning environments. As a result, there is an increasing need for innovative strategies that can simplify learning without diluting academic standards. One promising approach is the use of artificial intelligence–based scaffolding tools, which provide learners with structured guidance to help them navigate demanding academic tasks. Purpose: This study aimed to explore how AI-supported planning tools influence students' ability to formulate research topics. Beyond this central objective, the study also examined whether differences in school type and gender affected students' performance when supported with AI scaffolding. Methods: The study adopted a pretest–posttest randomized controlled trial involving 94 students. Participants were randomly assigned to either the experimental group, which received AI-scaffolded support, or the control group, which did not receive any support. The Research Topic Quality Scale, adapted from Creswell and Clark (2018), was employed to evaluate the clarity, feasibility, and alignment of research topics generated by the students. Data analysis was conducted using ANCOVA to examine both main effects and interaction effects of the independent variables. Results: The findings revealed that students who had access to the AI-scaffolded tool performed significantly better in developing their research topics compared to those in the control group. In addition, results showed that private school students achieved higher scores than their counterparts in public schools, suggesting that resource availability played a role. Gender, however, did not emerge as a significant factor in students’ performance. No significant interaction effects were found between school type, gender, and intervention. Conclusion: The study demonstrates that AI-based scaffolding has the potential to enhance the ability of students with mild intellectual disabilities to engage with higher-order academic tasks, such as formulating research topics. While the intervention proved effective for both male and female learners, the differences observed between private and public schools highlight persistent inequalities in educational resources that still need to be addressed. Originality/Value: This research contributes to the growing body of evidence supporting the integration of artificial intelligence into inclusive education. By demonstrating that AI-driven scaffolding can enhance complex academic skills, the study underscores its value as a practical and innovative approach for promoting educational success among students with mild intellectual disabilities.