Factors Linked to AI Literacy in University Students

The Relationship of Artificial Intelligence Literacy With Academic Achievement, Reading Habits, Smartphone Addiction, and Internet Addiction Among University Students

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.

Study Overview

• Status: Completed
• Conditions: Academic Acheivement; Internet Addiction; Smartphone Addiction; Technology Literacy; Reading Habits
• Intervention / Treatment: Behavioral: Assessment of Artificial Intelligence Literacy

Detailed Description

Artificial Intelligence (AI), a transformative force within information technology, is a subfield of computer science that involves creating intelligent machines and software that act and respond similarly to humans. With the introduction of ChatGPT, an OpenAI product released in November 2022, the concept of artificial intelligence has gained further popularity. Historically, a significant milestone for AI was the Turing Test, introduced by Alan Turing in 1950 to measure a machine's ability to exhibit human-like behaviors. Following this, the development of expert systems in the 1960s-70s, neural networks in the 1980s, machine learning and data mining in the 1990s, and deep learning in the 2000s each marked pivotal points in the AI timeline . Within the realm of computing, AI is often described as a "man-made homo sapiens" species . AI systems possess foundational skills such as learning, reasoning, self-improvement through experiential learning, language comprehension, and problem-solving, and are programmed as simulations of human intelligence. AI and its applications are utilized to address complex issues across diverse fields-including science, healthcare, education, engineering, business, defense, entertainment, and advertising-by means of expert systems.

The rapid integration of AI technologies into daily life has made it essential for individuals to acquire knowledge and skills related to these technologies. AI literacy represents an understanding and awareness of core artificial intelligence concepts. In this context, AI literacy is a fundamental competency that enables individuals to understand, utilize, and critically evaluate AI technologies, recognizing both their benefits and limitations. Having AI literacy can help individuals understand and manage AI technologies, offering an opportunity to become more informed and capable individuals. Therefore, it has become essential for everyone today to possess and enhance their AI literacy.

Factors such as reading habits and levels of academic achievement may positively influence the development of AI literacy. Individuals who have regular reading habits typically develop critical thinking and in-depth analysis skills, which facilitate understanding and critically evaluating AI technologies. Similarly, individuals with high academic performance are often experienced in accessing and applying knowledge, making them more adaptable to the foundational skills required for gaining AI literacy.

However, behaviors like internet addiction and smartphone addiction, while facilitating access to AI technologies, may have an adverse effect on AI literacy. Internet addiction reinforces a habit of accessing information rapidly and superficially, which can reduce critical thinking and focus. Likewise, smartphone addiction, due to its provision of constant and superficial access to information, may diminish interest in the deep thinking processes required for AI literacy. Therefore, internet and smartphone addiction could act as barriers in the processes requiring deep thought, analysis, and accumulation of knowledge essential for AI literacy.

To our knowledge, there is no comprehensive study that examines AI literacy among university students in relation to academic achievement, reading habits, smartphone addiction, and internet addiction from a multifaceted perspective.

The aim of this study is to reveal the relationships between university students' AI literacy and their levels of academic achievement, reading habits, internet addiction, and smartphone addiction.

• Study Type: Observational
• Enrollment (Actual): 184

Contacts and Locations

Study Locations

• Turkey (Türkiye)
  • Ankara, Turkey (Türkiye)
    • Atılım University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult
• Accepts Healthy Volunteers: No

• 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.

Description

Inclusion Criteria:

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

Exclusion Criteria:

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

The group to be evaluated in terms of AI literacy

Behavioral: Assessment of Artificial Intelligence Literacy; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Nagihan Acet

Publications and helpful links

General Publications

• 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.
• Young, K.S., Internet addiction test. Center for on-line addictions, 2009.
• 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.
• 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.
• 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.
• Çelebi, C., et al., Artificial intelligence literacy: An adaptation study. Instructional Technology and Lifelong Learning, 2023. 4(2): p. 291-306.
• 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.
• 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.
• 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.
• 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.
• 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.
• Turing, A.M., Computing machinery and intelligence. 2009: Springer.
• Muggleton, S., Alan Turing and the development of Artificial Intelligence. AI communications, 2014. 27(1): p. 3-10.
• Kamble, R. and D. Shah, Applications of artificial intelligence in human life. International Journal of Research-Granthaalayah, 2018. 6(6): p. 178-188.

Study record dates

Study Major Dates

• Study Start (Actual): November 15, 2024
• Primary Completion (Actual): March 15, 2025
• Study Completion (Actual): April 15, 2025

Study Registration Dates

• First Submitted: November 13, 2024
• First Submitted That Met QC Criteria: November 13, 2024
• First Posted (Actual): November 14, 2024

Study Record Updates

• Last Update Posted (Actual): November 25, 2025
• Last Update Submitted That Met QC Criteria: November 20, 2025
• Last Verified: November 1, 2025

More Information

Terms related to this study

• Keywords: artificial intelligence; internet addiction; smartphone addiction; reading habits
• Additional Relevant MeSH Terms: Technology Addiction; Behavior, Addictive; Compulsive Behavior; Impulsive Behavior; Behavior; Internet Addiction Disorder
• Other Study ID Numbers: Atılım University_5

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No