AI Use Motives Test (QAIUM)

The Questionnaire of Artificial Intelligence Use Motives (QAIUM) was created by researchers Eyüp Yurt and İsmail Kaşarcı in 2024 to provide an empirical framework for understanding why individuals adopt and integrate artificial intelligence into their personal and professional routines. Rather than treating technology adoption as a simple binary choice, Yurt and Kaşarcı drew upon expectancy-value theory to map the distinct psychological factors that inspire people to learn, use, and commit time to emerging AI tools. Their research highlights that people approach technology with varying expectations, goals, and perceived trade-offs.

The QAIUM framework evaluates five core dimensions that together profile your distinct driver of AI use. Ability Expectancy reflects your confidence in your personal skills, technical competence, and self-efficacy when operating AI tools relative to your peers. Attainment Value measures how central mastering AI innovations is to your identity, personal goals, and sense of stay-up-to-date achievement. Utility Value assesses the practical, functional benefits you gain from artificial intelligence, such as streamlining daily tasks, increasing overall productivity, and boosting professional effectiveness.

The remaining two dimensions explore your emotional engagement and resource allocation. Intrinsic Value captures the inherent enjoyment, curiosity, and pleasure you experience while exploring and experimenting with AI applications. Perceived Cost examines your willingness to invest time, energy, and effort into learning new tools, as well as your readiness to sacrifice other activities to gain proficiency. Together, these five dimensions reveal whether your engagement with technology is guided primarily by practical necessity, personal interest, social identity, confidence, or investment choices.

Educational and psychological research on motivation demonstrates that people rarely rely on a single motive when adopting complex tools like artificial intelligence. High utility value often prompts initial adoption when quick solutions are required, but sustained long-term mastery usually depends on a combination of high intrinsic value and high ability expectancy. Conversely, individuals who perceive a high cost in terms of time or cognitive effort may hesitate to adopt new systems unless their perceived utility or attainment value outweighs those investments. Understanding your own profile helps clarify why you adopt certain tools enthusiastically while overlooking others.

To give your scores meaningful context, the chart places your individual profile alongside estimated population comparison markers for each scale. These comparison markers are approximate estimates rescaled from general trends observed in published artificial intelligence adoption samples, rather than validated population percentiles or clinical norms for this specific web test. Treat these benchmark figures as a helpful comparative backdrop for reading your relative strengths across the five drivers, rather than a strict standard or rigid benchmark.

This test is provided exclusively for educational and self-reflection purposes. It is not a diagnostic tool, psychological evaluation, or professional assessment, and it does not offer career or technical advice. Your results reflect your self-reported motives for engaging with artificial intelligence at this point in time, rather than a permanent measure of your capability or potential. This test is based on the Questionnaire of Artificial Intelligence Use Motives (QAIUM) and is not affiliated with Eyüp Yurt, İsmail Kaşarcı, or Middle East Technical University.

References

• Yurt, E. & Kasarci, I. (2024). A Questionnaire of Artificial Intelligence Use Motives: A Contribution to Investigating the Connection between AI and Motivation. International Journal of Technology in Education, 7(2), 308-325.