THE ROLE OF FUTURE STUDIES IN DEVELOPING ARTIFICIAL INTELLIGENCE APPLICATION SKILLS
- Authors
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Dunea Taleb Kazim
Administrative Polytechnic College – Baghdad Middle Technical University, Baghdad, Iraq
Author
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- Keywords:
- Future Studies , Artificial Intelligence, AI Skills Development, Futures Literacy, Technology Education, Prospective Thinking, Competency-Based Learning , Educational Innovation
- Abstract
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This work examines the pertinence of futures studies in facilitating the demonstration of artificial intelligence (AI) for teachers, students, and practitioners in agencies. As AI technologies continue to permeate aspects of daily living, so too do implications for the capacity to anticipate, respond to, and strategically leverage such technologies. Futures studies—like an interdisciplinary field of long-term trends scouting with scenario planning and prospective tools—has a systematized way of thinking that is shareable at all levels to think ahead about AI-related changes. This case study research is conducted in a mixed-method manner by (i) survey (n=320) quantifying respondents from educational institutions, technology companies, and government organizations and (ii) qualitative case study with integration of literature. Theoretical Theoretical Underpinnings The theoretical lens is drawn from Bell’s [12] core theory of futures studies and is guided by Vygotsky’s zone of proximal development (ZPD), intersecting 21st Century skill education models to provide a conceptual framework for AI skill development through futures-based learning. The results showed that there was a statistically significant higher level of achievement in skill development of AI in the future studies-based institutions (p<0.001) with a mean gain rate of 34.7 % in competency compared to the traditional training system. Also, scenario-based learning interventions were the most promising for transfer-desired AI application skills in terms of the constructs of machine learning literacy, ethical AI decision making, and human-AI teamwork. This research unveils a crucial gap between current educational systems and future skills that individuals need to be employable with AI. Recommendations include adaptation of futures literacy approaches in AI education, the establishment of organizational foresight functions, and development of adaptive learning ecosystems that evolve in tandem with technological advances.
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- Published
- 2026-07-10
- Issue
- Vol. 2 No. 7 (2026)
- Section
- Articles
- License
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This work is licensed under a Creative Commons Attribution 4.0 International License.
