ChatGPT has taken the world by storm, setting a record for the fastest app to reach a 100 million users, which it hit in two months. The implications of this tool are far-reaching, universities...

ChatGPT is used most widely among those between ** and ** around the world. The youngest group, those under **, are the second largest userbase, and together those under ** account for over ** percent of ChatGPT users. It is perhaps unsurprising that the younger age brackets use the chatbot more than older as that is the common trend with new technologies. Male users were far more numerous than female users, with males representing over ** percent of total users in 2023.

In a survey conducted across **** Southeast Asian countries in February 2023, almost half of the respondents selected collection of personal data as one of the concerns they had regarding the usage of chatbots like ChatGPT. In contrast, ethical issues related to data privacy and intellectual property were a concern for ** percent of the respondents.

Objective- The primary research objective for this study is to develop a reliable and valid scale to measure higher education students' knowledge, attitudes, and usage of GenAI.

GenAI refers to a type of artificial intelligence that generates content in response to prompts (Dwivedi et al., 2023 cited in Chiu, 2024). These prompts can include text, software code, images, videos, and music. The inception and rapid advancement of Generative AI in society has had a considerable impact on higher education.

Educators and students are beginning to apply GenAI for various purposes, including creating and enhancing learning environments, study resources, lesson plans, idea generation, data analysis, text summarisation and enhancement, and streamlining administrative processes (Francis, Jones & Smith, 2025; Freeman, 2025; Tillmans et al., 2025).

GenAI has the potential to enhance learning experiences by aiding learners, saving time, and empowering users to control their own educational journey. However, understanding students' knowledge, attitudes, and usage of GenAI is crucial for integrating these technologies effectively into educational settings. Despite the growing interest in GenAI, there is limited research on how students understand, perceive and use GenAI in their studies, and to our knowledge no freely available validated scale for effectively and quickly measuring knowledge, attitudes, and usage in a student cohort.

Understanding students' knowledge about GenAI is crucial for assessing their ability to engage with these technologies effectively and for the design of AI literacy training. Research carried out by Chan and Zhou (2023) on undergraduate and postgraduate students indicated that while students generally possess a basic understanding of GenAI applications and impacts, there is a significant gap in deeper technical knowledge and awareness of ethical implications. Educational interventions (e.g. online workshops) carried out by Putjorn and Putjorn (2023), emphasise the importance of integrating AI literacy into curricula to empower students with comprehensive knowledge of GenAI technologies which in turn prepares students for the modern job market where use of GenAI tools is becoming commonplace.

By studying people’s attitudes, we can better explain the decision-making and behaviour of individuals and communities, and create a supportive environment for responsible use (Cao et al., 2021). Factors that influence AI attitudes have been studied from demographic, personality, anxiety, and trust perspectives, with men reporting more positive attitudes towards AI (Liang and Lee, 2017, Schepman and Rodway, 2022). Regarding age and AI attitudes, the research results are contradictory (Kaya et al., 2022). However, most of the literature states that younger age is connected to more positive attitudes towards AI (Gillespie et al., 2021, Schepman and Rodway, 2022). Higher education has also been shown to relate to positive AI attitudes (European Commission, & Directorate-General for Communications Networks, Content and Technology, 2017, Neudert et al., 2020). However, a more comprehensive understanding of attitudes towards *Gen*AI among students will provide policy makers and educators with key insights needed to support student learning.

Research regarding students’ usage of AI is still emerging. Johnston et al. (2024) conducted a focus group where half of the students reported using or considering using GenAI for academic purposes. Although most students were supportive of using GenAI for grammar and spelling, most were unsupportive of the use of GenAI for assessment writing. Likewise, qualitative responses highlighted that students were unsupportive towards using GenAI for essay writing, as this was considered “cheating”, but GenAI could be used as an alternative to lecturers or to understand a concept. Smolansky et al. (2023) reported ‘moderate usage’ of GenAI tools among students for assignments and assessments relating to essay writing and coding. Findings from Smolansky et al. (2023) also highlighted concerns among students and educators regarding academic integrity and the use of GenAI in traditional assessments such as essay writing.

Currently, there is no comprehensive, validated and freely available tool specifically designed to measure students' knowledge, attitudes, and usage of GenAI. Indeed, knowledge, attitudes and usage are useful dimensions for measuring programs, products and technologies because understanding knowledge gaps, usage patterns, and attitudinal barriers helps in designing targeted interventions, policies, and educational programs. The creation of a questionnaire using these dimensions and tailored to GenAI will fill a measurement gap, providing a robust scale for future research and ongoing assessment. This scale will enable consistent tracking of changes over time and the effectiveness of interventions aimed at improving GenAI literacy and engagement. In conclusion, this study aims to fill the gap in the existing literature by developing and validating a scale to measure students' knowledge, attitudes, and usage of GenAI, and by exploring the factors that influence these dimensions. The findings will provide valuable insights for educators and policymakers to design GenAI education programs that are responsive to students' needs and concerns.

References Allam, H., Dempere, J., Akre, V., Parakash, D., Mazher, N., & Ahamed, J. (2023, May). Artificial intelligence in education: an argument of Chat-GPT use in education. In 2023 9th International Conference on Information Technology Trends (ITT) (pp. 151-156). IEEE. Bergdahl, J., Latikka, R., Celuch, M., Savolainen, I., Mantere, E. S., Savela, N., & Oksanen, A. (2023). Self-determination and attitudes toward artificial intelligence: Cross-national and longitudinal perspectives. Telematics and Informatics, 82, 102013. Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312. Chan, C. K. Y., & Zhou, W. (2023). Deconstructing student perceptions of generative AI (GenAI) through an expectancy value theory (EVT)-based instrument. arXiv preprint arXiv:2305.01186. Cotton, D. R., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in education and teaching international, 61(2), 228-239. Eurobarometer, S. (2017). 460-Attitudes Towards the Impact of Digitisation and Automation on Daily Life. European Commission, Brussels, EU. Francis, N. J., Jones, S., & Smith, D. P. (2025). Generative AI in higher education: Balancing innovation and integrity. British Journal of Biomedical Science, 81, 14048. Freeman, J. (2025). Student Generative AI Survey 2025. Technical report, HEPI, URL https://www. hepi. ac. uk/2025/02/26/student-generative-ai-survey-2025. Gillespie, N., Lockey, S., & Curtis, C. (2021). Trust in artificial intelligence: A five country study. Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Khan, I. H. (2023). Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(2), 100115. Johnston, H., Wells, R. F., Shanks, E. M., Boey, T., & Parsons, B. N. (2024). Student perspectives on the use of generative artificial intelligence technologies in higher education. International Journal for Educational Integrity, 20(1), 2. Liang, Y., & Lee, S. A. (2017). Fear of autonomous robots and artificial intelligence: Evidence from national representative data with probability sampling. International Journal of Social Robotics, 9, 379-384. Neudert, L. M., Knuutila, A., & Howard, P. N. (2020). Global attitudes towards AI, machine learning & automated decision making. Working paper 2020.10, Oxford Commission on AI & Good Governance. https://oxcaigg. oii. ox. ac. uk. Putjorn, T., & Putjorn, P. (2023, October). Augmented Imagination: Exploring Generative AI from the Perspectives of Young Learners. In 2023 15th International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 353-358). IEEE. Schepman, A., & Rodway, P. (2023). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human–Computer Interaction, 39(13), 2724-2741. Smolansky, A., Cram, A., Raduescu, C., Zeivots, S., Huber, E., & Kizilcec, R. F. (2023, July). Educator and student perspectives on the impact of generative AI on assessments in higher education. In Proceedings of the tenth ACM conference on Learning@ Scale (pp. 378-382) Stokel-Walker, C. (2022). AI bot ChatGPT writes smart essays-should academics worry?. Nature. Tillmanns, T., Salomão Filho, A., Rudra, S., Weber, P., Dawitz, J., Wiersma, E., ... & Reynolds, S. (2025). Mapping Tomorrow’s Teaching and Learning Spaces: A Systematic Review on GenAI in Higher Education. Trends in Higher Education, 4(1), 2. Yu, H. (2023). Reflection on whether Chat GPT should be banned by academia from the perspective of education and teaching. Frontiers in Psychology, 14, 1181712.

Unsurprisingly, those between 18 and 24 were the most likely to use ChatGPT among the various age groups, with ** percent of respondents saying so. This is likely due to younger people adopting tech at a faster rate than older generations.

According to a survey of adults in the United States conducted in January 2023, ** percent of respondents age between 30 to 44 years old claimed to have used ChatGPT to generate text themselves. In comparison, 48 percent of respondents aged between 18 and 29 years old and another ** percent of respondents aged between 30 to 44 years old reported having seen text being generated by the AI technology for someone else. Meanwhile, ** percent of those older than 65 years old respondents claimed to have never used nor seen anyone else use it.

In a survey conducted across four Southeast Asian countries in February 2023, ** percent of the respondents in Singapore selected the collection of personal data as one of the concerns they had regarding the usage of chatbots like ChatGPT. In contrast, this was an issue for ** percent of respondents in Indonesia.

A 2023 survey found that approximately ** percent of respondents in Indonesia reported using ChatGPT on a weekly basis for various purposes, from work to entertainment. Meanwhile, around ** percent of respondents stated that they rarely used ChatGPT.

Market Overview The global AI Skincare Advisor market is projected to reach a valuation of XXX million by 2033, exhibiting a CAGR of XX% during the forecast period. The increasing demand for personalized skincare recommendations and the growing adoption of AI technologies are driving this growth. These advisors use advanced algorithms to analyze an individual's skin type, concerns, and usage patterns, providing tailored skincare routines that enhance efficacy and minimize irritation. Market Dynamics Key drivers of the market include the rising prevalence of skin concerns, the increasing disposable income for skincare products, and the growing awareness of AI's benefits in skincare. Trends such as the integration of telehealth platforms and the rise of smart beauty devices are further contributing to market expansion. However, restraints like the high cost of AI technology and regulatory concerns regarding data privacy pose challenges. The market is segmented by type (online use, mobile app), application (beauty salon, home use), and region (North America, Europe, Asia Pacific). Major players in the market include Revieve, SKINMART, KIKO, Haut.AI, and Chat Gpt.

In January 2024, ChatGPT online domain chat.openai.com registered over **** percent of its traffic as originating in the United States. Users based in India generated approximately **** percent of the total visits to the chatbot platform, while users in Indonesia accounted for *** percent of the total visits to the website. Visits from Brazil represented the fourth-largest group for the platform, generating more than **** percent of the total traffic recorded in the examined period.

ChatGPT, an artificial intelligence (AI) powered chatbot, is most used by companies in the technical and education industries, with over *** companies using it in 2023. It is perhaps unsurprising that the technical field has embraced the use of ChatGPT, but it is interesting that so many educational institutes have begun to use it. While other industries do utilize the OpenAI-made chatbot, there are less than *** institutions and companies that use ChatGPT in other industries. This is especially true of agriculture, cultural, and legal industries, where only a single company is using ChatGPT in 2023.

Comparison of Represents the average of math benchmarks in the Artificial Analysis Intelligence Index (AIME 2024 & Math-500) by Model

Comprehensive comparison of Latency (Time to First Token) vs. Output Speed (Output Tokens per Second) by Model

Comparison of Seconds to Output 500 Tokens, including reasoning model 'thinking' time; Lower is better by Model

As of April 2023, more than **** million Poles were using ChatGPT.

Comprehensive comparison of Artificial Analysis Intelligence Index vs. Seconds to Output 500 Tokens, including reasoning model 'thinking' time by Model

In March 2025, ChatGPT’s mobile app recorded over 64.26 million App Store and Google Play downloads worldwide. Google's Gemini AI Assistant mobile app was released on February 8, 2024, and was initially available in the U.S. market only. In the same month, the app registered around 13.92 million downloads. Regional preferences shape AI app adoption ChatGPT has a strong global presence with over 400.61 million monthly active users in February 2025, but regional preferences vary. In the United States, ChatGPT had a 45 percent download market share, compared to Google Gemini's 11 percent. However, Gemini emerged as the preferred generative AI app in India, representing a 52 percent market share. This competitive landscape now also includes Chinese-based players like ByteDance's Doubao and DeepSeek, indicating an even more diverse and evolving AI worldwide ecosystem. The AI-powered revolution in online search The global AI market has experienced substantial growth, exceeding 184 billion U.S. dollars in 2024 and projected to surpass 826 billion U.S. dollars by 2030. This expansion is mirrored in user behavior, with around 15 million adults in the United States using AI-powered tools as their first option for online search in 2024. Additionally, 68 percent of U.S. adults reported the use of AI-powered search engines for exploring new topics in 2024, with another 44 percent of respondents utilizing these tools to learn or explain concepts.

Comprehensive comparison of Artificial Analysis Intelligence Index vs. Output Speed (Output Tokens per Second) by Model

Comparison of Represents the average of coding benchmarks in the Artificial Analysis Intelligence Index (LiveCodeBench & SciCode) by Model

Comprehensive comparison of Artificial Analysis Intelligence Index vs. Output Tokens Used in Artificial Analysis Intelligence Index (Log Scale) by Model