Factors Linked to AI Literacy in University Students

NCT06689319 | Completed

• 1 other identifier: Atılım University_5
• Study Type: observational
• Target: 184
• Locations: 1 country
• Sites: 1

Brief Summary

This study investigates the relationships between artificial intelligence (AI) literacy and factors such as academic achievement, reading habits, smartphone addiction, and internet addiction among university students. As AI technologies become increasingly integrated into daily life, AI literacy-necessary for understanding and evaluating AI-is emerging as a critical skill. While factors like academic success and regular reading habits may enhance AI literacy, behaviors like smartphone and internet addiction may have an adverse effect by promoting superficial information access over deeper critical engagement. This prospective, observational, and cross-sectional study will assess AI literacy using the Artificial Intelligence Literacy Scale and analyze its association with academic and behavioral factors. The study will be conducted among participants aged 18-35 in the Physiotherapy and Rehabilitation Department Laboratory at Atılım University. Data will be evaluated using descriptive statistics, correlation analyses (Pearson or Spearman, depending on distribution), and significance testing. The results may highlight the impact of academic and behavioral factors on AI literacy, offering insights for educational strategies aimed at fostering critical AI competencies.

Conditions

Technology Literacy; Reading Habits; Smartphone Addiction; Internet Addiction; Academic Acheivement

Keywords

artificial intelligence; reading habits; smartphone addiction; internet addiction

Outcome Measures

Primary Outcomes (4)

• Assessment of reading habits

  Assessment of reading habits The Self-Report Habit Index will be used to assess reading habits . The Reading Habits Questionnaire is a 12-item instrument designed to assess individuals' reading habits, covering dimensions such as reading frequency, duration, preferred materials (books, magazines, online content, etc.), reading purpose, and reading environment. The questionnaire allows participants to respond on a 5-point Likert scale (1: Never, 5: Always). The total score obtained is used to interpret an individual's reading habits: low scores indicate infrequent reading, moderate scores represent regular but not intensive reading habits, and high scores reflect frequent reading of diverse materials. This assessment helps determine the level of an individual's reading habits and identify areas for potential improvement.

  Day 1

• Assessment of smartphone addiction

  Smartphone addiction will be assessed using the Smartphone Addiction Scale - Short Form. This is a 10-item scale used to evaluate individuals' smartphone usage habits.Each item is scored from 1 (Strongly Disagree) to 6 (Strongly Agree), with a minimum total score of 10 and a maximum of 60. Higher scores indicate a greater risk of addiction and provide a quick assessment.

  Day 1

• Assessment of internet addiction

  Internet addiction will be assessed using the Internet Addiction Scale - Short Form, an instrument designed to evaluate individuals' internet usage habits. Originally developed by Young (1998), the scale has been adapted as a short form consisting of 6 items for a quick assessment of internet addiction \[14\]. The Turkish version will be used \[15\]. Each item is rated from 1 (Never) to 5 (Always), with a total score ranging from 6 to 30. Higher scores indicate an increased risk of internet addiction.

  Day 1

• Assessment of academic achievement

  The level of academic achievement will be assessed based on the cumulative grade point average (GPA) from the previous semester. This measure provides an objective indicator of students' overall academic performance, capturing their sustained efforts and intellectual engagement in coursework.

  Day 1

Study Arms (1)

The group to be evaluated in terms of AI literacy

Behavioral: Assessment of Artificial Intelligence Literacy

Interventions

Assessment of Artificial Intelligence Literacy BEHAVIORAL

The Artificial Intelligence Literacy Scale will be used to determine the level of AI literacy.. The scale is a 12-item instrument designed to measure individuals' knowledge and skills in AI awareness, usage, evaluation, and ethical considerations. Items are rated on a Likert scale from 1 to 7 (1: Strongly Disagree, 7: Strongly Agree), with some items reverse-coded (items 2, 5, and 11). The minimum possible score on the scale is 12, and the maximum score is 84; a higher score indicates a higher level of AI literacy. The Turkish version of the scale will be used in this study.

The group to be evaluated in terms of AI literacy

Eligibility Criteria

• Age: 18 Years - 35 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

This study will involve a selected population of university students aged 18-35, recruited from Atılım University. The participants, both male and female, will be screened based on specific inclusion and exclusion criteria, such as literacy, willingness to participate, and ability to cooperate. The sample includes individuals with varied academic backgrounds relevant to the study objectives.

You may qualify if:

• Being between 18-35 years of age.
• Willingness to participate after receiving detailed information about the study's purpose and methodology.

You may not qualify if:

• Missing responses in questionnaires.
• Illiteracy.
• Inability to cooperate.

Sponsors & Collaborators

• Nagihan Acet: lead

Study Sites (1)

Atılım University

Ankara, Turkey (Türkiye)

Location

Related Publications (14)

• Kutlu, M., et al., Turkish adaptation of Young's Internet Addiction Test-Short Form: A reliability and validity study on university students and adolescents/Young Internet Bagimliligi Testi Kisa Formunun Turkce uyarlamasi: Universite ogrencileri ve ergenlerde gecerlilik ve guvenilirlik calismasi. Anadolu Psikiyatri Dergisi, 2016. 17(S1): p. 69-77.

  RESULT

• Young, K.S., Internet addiction test. Center for on-line addictions, 2009.

  RESULT

• Noyan, C.O., et al., Validity and reliability of the Turkish version of the Smartphone Addiction Scale-Short version among university students/Akilli Telefon Bagimliligi Olceginin Kisa Formunun universite ogrencilerinde Turkce gecerlilik ve guvenilirlik calismasi. Anadolu Psikiyatri Dergisi, 2015. 16(S1): p. 73-82.

  RESULT

• Kwon M, Kim DJ, Cho H, Yang S. The smartphone addiction scale: development and validation of a short version for adolescents. PLoS One. 2013 Dec 31;8(12):e83558. doi: 10.1371/journal.pone.0083558. eCollection 2013.

  PMID: 24391787 RESULT

• Verplanken, B. and S. Orbell, Reflections on past behavior: a self-report index of habit strength 1. Journal of applied social psychology, 2003. 33(6): p. 1313-1330.

  RESULT

• Çelebi, C., et al., Artificial intelligence literacy: An adaptation study. Instructional Technology and Lifelong Learning, 2023. 4(2): p. 291-306.

  RESULT

• Wang, B., P.-L.P. Rau, and T. Yuan, Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behaviour & information technology, 2023. 42(9): p. 1324-1337.

  RESULT

• Kong, S.-C., W.M.-Y. Cheung, and G. Zhang, Evaluating an artificial intelligence literacy programme for developing university students' conceptual understanding, literacy, empowerment and ethical awareness. Educational Technology & Society, 2023. 26(1): p. 16-30.

  RESULT

• Laupichler, M.C., et al., Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 2022. 3: p. 100101.

  RESULT

• Copeland, B.J. and D. Proudfoot, Artificial intelligence: History, foundations, and philosophical issues, in Philosophy of psychology and cognitive science. 2007, Elsevier. p. 429-482.

  RESULT

• Haenlein, M. and A. Kaplan, A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review, 2019. 61(4): p. 5-14.

  RESULT

• Turing, A.M., Computing machinery and intelligence. 2009: Springer.

  RESULT

• Muggleton, S., Alan Turing and the development of Artificial Intelligence. AI communications, 2014. 27(1): p. 3-10.

  RESULT

• Kamble, R. and D. Shah, Applications of artificial intelligence in human life. International Journal of Research-Granthaalayah, 2018. 6(6): p. 178-188.

  RESULT

MeSH Terms

Conditions

Internet Addiction Disorder

Condition Hierarchy (Ancestors)

Technology Addiction; Behavior, Addictive; Compulsive Behavior; Impulsive Behavior; Behavior

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Asst. Prof.

Study Record Dates

• First Submitted: November 13, 2024
• First Posted: November 14, 2024
• Study Start: November 15, 2024
• Primary Completion: March 15, 2025
• Study Completion: April 15, 2025
• Last Updated: November 25, 2025

Record last verified: 2025-11

Locations

TU (1)