# Evaluating Artificial Intelligence on the Efficacy of Preference Assessments for Preservice Speech-Language Pathologists

## TL;DR

This study compares AI-based training to traditional methods for teaching preference assessments to future speech-language pathologists, finding AI more effective and acceptable.

## Contribution

The novel use of artificial intelligence to train preservice SLPs in preference assessments, showing improved performance and acceptability compared to traditional methods.

## Key findings

- AI increased implementation fidelity for most participants and reduced scoring errors.
- AI significantly reduced assessment duration for four participants.
- Participants found AI more acceptable and effective for meaningful outcomes.

## Abstract

Individuals with intellectual and developmental disabilities (IDD) face many barriers to meaningful inclusion, including limited language and communication skills. Professionals, such as speech-language pathologists (SLPs), can provide personalized instruction to promote skill development and inclusion. Providing opportunities for individuals to express preferences and choice, such as the multiple stimulus without replacement preference assessment (MSWO; DeLeon & Iwata 1996), within these programs, further increases skill acquisition and social interaction. However, limitations in professionals’ knowledge and skills in performing assessments can be another barrier to meaningful inclusion for individuals with IDD and traditional training methods can be challenging and time consuming. The purpose of the current study was to compare the use of artificial intelligence with traditional pen and paper self-instructional MSWO training methods for five preservice SLPs. Fidelity of implementation and duration of assessment were measured. Results demonstrated a large increase in implementation fidelity for two participants, a moderate increase for two participants and a slight increase for the remaining participant while using artificial intelligence. All participants demonstrated a decrease in scoring errors using artificial intelligence. Regarding duration of implementation, artificial intelligence resulted in a significant reduction for four participants and a moderate reduction for the remaining participant. Results of the follow-up survey suggest that all adult participants and both child participants found that artificial intelligence had a higher treatment acceptability and was more effective at producing socially significant outcomes than traditional methods. Recommendations for clinicians and future research are discussed.