Development of an Instrument to Measure AI-integrated Self-Directed Learning Personal Attributes for Global Language Learners

• In Event: AERA Roundtable Session Thursday 9:50 am Four Seasons Ballroom 4
  In Roundtable Session: Technology as an Intervention of Learning (Table 3)

Abstract

The AI-Integrated Self-Directed Learning (AI-SDL) Framework conceptualize SDL in generative AI-assisted language learning contexts. To measure AI-SDL personal attributes (AI-SDL-PA) among global language learners, 63 items were developed through literature review and expert input. Exploratory factor analysis revealed a 9-factor structure within five main constructs: attitude, self-efficacy, motivation, strategy use, and resource use. After eliminating low- and cross-loading items, the final 44-item scale explained 63.7% of variance, with factor loadings ranging from .384 to .954. Cronbach's alpha values (.730 to .921) indicated high internal consistency. This study provides a validated instrument to assess AI-SDL personal attributes, offering researchers and educators a tool to evaluate and enhance AI-supported language learning interventions. Future research directions and practical implications are discussed.

Authors

• Belle Li, Purdue University

• Zhuo Zhang, Towson University

• Victoria Lynn Lowell, Purdue University

• Chaoran Wang, Colby College

• Curtis J. Bonk, Indiana University