Prompt Engineering and Student Well-Being: Exploring AI Literacy, Self-Learning, and Mental Health in UAE Higher Education

Document Type

Book Chapter

Source of Publication

Implications for Students Mental Health in the Digital Age AI and Cyber Behavior

Publication Date

3-26-2026

Abstract

This chapter examines how AI literacy and prompt engineering shape students’ psychological well-being, motivation, and self-regulated learning in UAE higher education. Using aggregated findings from an undergraduate General Education cohort, the analysis links familiarity with Generative AI, frequency and manner of use, and emotional outcomes such as confidence, anxiety, and digital fatigue. Findings indicate that moderate, purposeful engagement is correlated with confidence, whereas excessive or unguided use is associated with cognitive overload and digital imbalance. Students with learning difficulties report lower engagement and higher frustration, underscoring inequities in digital access and support. AI literacy, particularly skilled prompt design, emerges as a protective factor that may mitigate anxiety, curb misuse, and enhance self-regulation. The chapter recommends embedding inclusive AI literacy, scaffolded prompt engineering, mental health awareness, and digital wellness practices into General Education curricula, aligned with the UAE National Agenda and SDGs 3 and 4.

ISBN

[9798337342221, 9798337342238, 9798337342245]

ISSN

2327-0411

Publisher

IGI Global Scientific Publishing

First Page

405

Last Page

424

Disciplines

Computer Sciences | Medicine and Health Sciences

Keywords

Digital literacy (0.57) | Psychology (0.53) | Information overload (0.47) | Digital learning (0.46) | Mental health (0.44) | Medical education (0.44) | Cognition (0.42) | Generative grammar (0.41) | Pedagogy (0.35) | Applied psychology (0.34) | Student engagement (0.32) | Higher education (0.32) | Educational technology (0.31) | Literacy (0.31) | Engineering education (0.3) | Technological literacy (0.27) | Key (lock) (0.27) | Cognitive load (0.26) | Training (meteorology) (0.26) | Engineering (0.26) | Computer science (0.26) | User engagement (0.26)

Scopus ID

105036467927

Indexed in Scopus

yes

Open Access

no

Link to Full Text

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