Unpacking GenAI literacy: The role of individual drivers and engagement levels

Mohammadreza Farrokhnia; Leonie Fischer; Ghasem Salimi; Seyyed Kazem Banihashem; Omid Noroozi

doi:10.3102/2278159

Unpacking GenAI literacy: The role of individual drivers and engagement levels

Mohammadreza Farrokhnia
, Leonie Fischer (Contributor)
, Ghasem Salimi (Contributor)
, Seyyed Kazem Banihashem
, Omid Noroozi

Instructional Technology

Research output: Contribution to conference › Paper › peer-review

9 Downloads (Pure)

Abstract

As Generative Artificial Intelligence (GenAI) tools become more common in higher education, the need for AI literacy among students has grown. This study proposed and tested a theoretical framework linking individual factors, i.e., critical thinking and self-regulation, levels of engagement (deep and surface), and AI literacy. The sample consisted of 301 university students from Iran. Data were analyzed using structural equation modeling and mediation analysis across 12 hypotheses. Results showed that deep engagement, self-regulation, and critical thinking were positively associated with AI literacy, while surface engagement was not. Self-regulation was not significantly related to either engagement type, whereas critical thinking was linked to deep but not surface engagement. These findings offer insights for designing strategies to foster meaningful student interaction with GenAI tools.

Original language	English
Number of pages	16
DOIs	https://doi.org/10.3102/2278159
Publication status	Published - 10 Apr 2026
Event	2026 American Educational Research Association Annual Meeting, AERA 2026: Unforgetting Histories and Imagining Futures: Constructing a New Vision for Education Research - JW Marriott LA Live, Los Angeles, United States Duration: 8 Apr 2026 → 12 Apr 2026 https://www.aera.net/AERA2026

Conference

Conference	2026 American Educational Research Association Annual Meeting, AERA 2026
Country/Territory	United States
City	Los Angeles
Period	8/04/26 → 12/04/26
Internet address	https://www.aera.net/AERA2026

Keywords

Artificial Intelligence
AI Literacy
Self-regulation
Critical thinking
Engagement
Higher Education

Access to Document

10.3102/2278159

Conference paperAccepted author version, 542 KB

Cite this

@conference{aeb361e6ec29428f800407f7a1669a0b,

title = "Unpacking GenAI literacy: The role of individual drivers and engagement levels",

abstract = "As Generative Artificial Intelligence (GenAI) tools become more common in higher education, the need for AI literacy among students has grown. This study proposed and tested a theoretical framework linking individual factors, i.e., critical thinking and self-regulation, levels of engagement (deep and surface), and AI literacy. The sample consisted of 301 university students from Iran. Data were analyzed using structural equation modeling and mediation analysis across 12 hypotheses. Results showed that deep engagement, self-regulation, and critical thinking were positively associated with AI literacy, while surface engagement was not. Self-regulation was not significantly related to either engagement type, whereas critical thinking was linked to deep but not surface engagement. These findings offer insights for designing strategies to foster meaningful student interaction with GenAI tools. ",

keywords = "Artificial Intelligence, AI Literacy, Self-regulation, Critical thinking, Engagement, Higher Education",

author = "Mohammadreza Farrokhnia and Leonie Fischer and Ghasem Salimi and Banihashem, \{Seyyed Kazem\} and Omid Noroozi",

year = "2026",

month = apr,

day = "10",

doi = "10.3102/2278159",

language = "English",

note = "2026 American Educational Research Association Annual Meeting, AERA 2026 : Unforgetting Histories and Imagining Futures: Constructing a New Vision for Education Research ; Conference date: 08-04-2026 Through 12-04-2026",

url = "https://www.aera.net/AERA2026",

}

Unpacking GenAI literacy: The role of individual drivers and engagement levels. / Farrokhnia, Mohammadreza; Fischer, Leonie (Contributor); Salimi, Ghasem (Contributor) et al.
2026. Paper presented at 2026 American Educational Research Association Annual Meeting, AERA 2026, Los Angeles, California, United States.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Unpacking GenAI literacy

T2 - 2026 American Educational Research Association Annual Meeting, AERA 2026

AU - Farrokhnia, Mohammadreza

AU - Banihashem, Seyyed Kazem

AU - Noroozi, Omid

A2 - Fischer, Leonie

A2 - Salimi, Ghasem

PY - 2026/4/10

Y1 - 2026/4/10

N2 - As Generative Artificial Intelligence (GenAI) tools become more common in higher education, the need for AI literacy among students has grown. This study proposed and tested a theoretical framework linking individual factors, i.e., critical thinking and self-regulation, levels of engagement (deep and surface), and AI literacy. The sample consisted of 301 university students from Iran. Data were analyzed using structural equation modeling and mediation analysis across 12 hypotheses. Results showed that deep engagement, self-regulation, and critical thinking were positively associated with AI literacy, while surface engagement was not. Self-regulation was not significantly related to either engagement type, whereas critical thinking was linked to deep but not surface engagement. These findings offer insights for designing strategies to foster meaningful student interaction with GenAI tools.

AB - As Generative Artificial Intelligence (GenAI) tools become more common in higher education, the need for AI literacy among students has grown. This study proposed and tested a theoretical framework linking individual factors, i.e., critical thinking and self-regulation, levels of engagement (deep and surface), and AI literacy. The sample consisted of 301 university students from Iran. Data were analyzed using structural equation modeling and mediation analysis across 12 hypotheses. Results showed that deep engagement, self-regulation, and critical thinking were positively associated with AI literacy, while surface engagement was not. Self-regulation was not significantly related to either engagement type, whereas critical thinking was linked to deep but not surface engagement. These findings offer insights for designing strategies to foster meaningful student interaction with GenAI tools.

KW - Artificial Intelligence

KW - AI Literacy

KW - Self-regulation

KW - Critical thinking

KW - Engagement

KW - Higher Education

U2 - 10.3102/2278159

DO - 10.3102/2278159

M3 - Paper

Y2 - 8 April 2026 through 12 April 2026

ER -

Unpacking GenAI literacy: The role of individual drivers and engagement levels

Abstract

Conference

Keywords

Access to Document

Fingerprint

Cite this