Designing, Developing, and Validating a Measure of Undergraduate Students' Conceptions of Artificial Intelligence in Education

Autor/inn/en	Cheng, Li; Umapathy, Karthikeyan; Rehman, Muhammad; Ritzhaupt, Albert; Antonyan, Kristine; Shidfar, Poorya; Nichols, James; Lee, Minyoung; Abramowitz, Brian
Titel	Designing, Developing, and Validating a Measure of Undergraduate Students' Conceptions of Artificial Intelligence in Education
Quelle	In: Journal of Interactive Learning Research, 34 (2023) 2, S.275-311 (37 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1093-023X
Schlagwörter	Undergraduate Students; Knowledge Level; Artificial Intelligence; Technology Uses in Education; Measures (Individuals); Test Construction; Test Validity; Psychometrics; Factor Analysis; Intelligent Tutoring Systems; Student Evaluation; Grading; School Holding Power; Dropouts; Individualized Instruction; Academic Achievement; Prediction + Suchen Sie Ihr Suchwort? Wissensbasis; Künstliche Intelligenz; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Messdaten; Testaufbau; Testvalidität; Psychometry; Psychometrie; Faktorenanalyse; Intelligentes Tutorsystem; Schulnote; Studentische Bewertung; Notengebung; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Individualisierender Unterricht; Schulleistung; Vorhersage
Abstract	The purpose of this research study is to design, develop, and validate an instrument for measuring undergraduate students' conceptions of artificial intelligence in education. Following systematic procedures, our team created a conceptual framework through an extant literature review and used it to create an initial item pool of 48-items across eight educational application domains of AI (Ahmad et al. (2020). We conducted think aloud protocols with a small group (N = 4) of undergraduate students and an expert review with researchers experienced in AI, psychometrics, and education (N = 3) to ensure the intended meaning of the language was aligned with our framework. Next, we recruited undergraduate student participants (N = 445) from two public universities and one state college in the southeastern United States to complete the final survey. Data were randomly split into two datasets, one for fitting a Confirmatory Factor Analysis model, and the other one for testing the model. The final model retained 37-items across the eight factors of conceptions of AI in education: (AI1) intelligent tutoring system, (AI2) students' grading and evaluation, (AI3) students' retention and dropout, (AI4) sentiment analysis in education, (AI5) recommendation systems, (AI6) classroom monitoring and visual analysis, (AI7) personalized learning, and (AI8) student's performance prediction. We present the results, provide interpretations and explanations, outline the limitations and delimitations of the study, and highlight recommendations for future research and practice. (As Provided).
Anmerkungen	Association for the Advancement of Computing in Education. P.O. Box 719, Waynesville, NC 28786. Tel: 828-246-9558; Fax: 828-246-9557; e-mail: info@aace.org; Web site: http://www.aace.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01