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.

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.

🧠 Awesome ChatGPT Prompts [CSV dataset]

This is a Dataset Repository of Awesome ChatGPT Prompts View All Prompts on GitHub

  License

CC-0

Silverhand SFT

  Dataset overview

Dataset containing human - AI conversations where the AI responses are written in the style of the fictional character Johnny Silverhand from the Cyberpunk universe (Mike Pondsmith, CD Project red).

  Sources

60% of the examples are re-written berkeley-nest/Nectar examples. (gpt-4 was used to re-write the AI responses) 40% are re-written interactions with ChatGPT (by gpt-4).

  Purpose

The dataset was used to finetune… See the full description on the dataset page: https://huggingface.co/datasets/jubba/silverhand_sft.

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

Dataset Card for Dataset Name

  Name

ChatGPT Jailbreak Prompts

  Dataset Summary

ChatGPT Jailbreak Prompts is a complete collection of jailbreak related prompts for ChatGPT. This dataset is intended to provide a valuable resource for understanding and generating text in the context of jailbreaking in ChatGPT.

  Languages

[English]

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

Comparison of Image Input Price: USD per 1k images at 1MP (1024x1024) by Model

Comparison of Represents the average of math benchmarks in the Artificial Analysis Intelligence Index (AIME 2025) by Model

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

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

Comparison of Tokens used to run all evaluations in the Artificial Analysis Intelligence Index by Model

Comparison of 𝜏²-Bench Telecom by Model

Comparison of Represents the average of coding benchmarks in the Artificial Analysis Intelligence Index (LiveCodeBench, SciCode & Terminal-Bench Hard) by Model

Comprehensive comparison of Artificial Analysis Intelligence Index vs. Context Window (Tokens) by Model

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

Comprehensive comparison of Output Speed (Output Tokens per Second) vs. Price (USD per M Tokens) by Model

Comprehensive comparison of Artificial Analysis Intelligence Index vs. Price (USD per M Tokens, Log Scale, More Expensive to Cheaper) by Model

Comparison of Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index by Model