Generative AI (GenAI) is a specific type of Artificial Intelligence that can create new content in the form of text, images, music, code, and various other forms of digital media by using machine learning or ‘training’. ChatGPT is an example of a GenAI application that has been trained on a vast amount of publicly available data. It was made freely available in November 2022, albeit with usage limitations. The launch sparked intense public interest, with initial speculation about what the effects might be for education, jobs, and for society in general. 

While ChatGPT was not the first model made available (e.g. GPT-2), it is an advanced model that allows for sophisticated interactions, with the remarkable capability of replicating human-like natural language processing. However, it cannot understand language. It is trained to detect complex patterns and assimilate that information into existing information (Bozkurt, 2023; UNESCO, 2023). As GenAI continues its rapid growth, evolving and improving outputs, there has been a wide range of perspectives, from those who have embraced the technology, those who oppose the technology, and some who are both enthusiastic and/or cautious. Gallant-Torres (2023) identifies the opposing extremes as ‘technophiles who defend it without regard to its risk and technophobes who reject it without considering its benefits.’

Research is beginning to emerge about the affordances and challenges of GenAI use in education. New skills are evolving with the use of GenAI such as ‘prompt engineering’, which is defined as the ‘art of designing, writing and fine-tuning prompts’ to elicit the most accurate and relevant responses from GenAI applications (Eager & Brunton, 2023). There have been significant opportunities that have already been identified as being embraced in higher education settings, such as integrating technology to promote learner-AI collaboration (Tan, Chen & Chua, 2023), personalised feedback and adaptive learning pathways (Eager & Brunton, 2023), automating processes, innovations in teaching and assessment and creating a more inclusive environment (Adiguzel, 2023; Moya & Eaton, 2023). However, the scope and the extent to which these practices have been adopted remain unclear. There are also complex issues emerging, as Farrelly and Baker (2023) highlight that ‘we are already seeing that minority and marginalised students are being accused of breaching academic integrity rules …perpetuating an existing inequitable pattern’. 

In 2021, UNESCO released ‘Recommendations on The Ethics of Artificial Intelligence’. The first statement highlights the importance of ‘the profound and dynamic positive and negative impacts of AI on societies, environment, ecosystems and human lives, including the human mind’ (UNESCO, 2021). The call for setting standards relating to AI technologies happened well before the launch of ChatGPT and the numerous other generative AI tools released since then. It is evident that GenAI has now been integrated into higher education settings (Ipek, 2023), amidst concerns about what the potential impacts could be on disciplinary knowledge and the assessment of key knowledge and skills. It is in this context that our research study aimed to examine the use of generative AI by academics and students in higher education, and their perceptions of the impact that the technology would have on teaching and learning. 

The key research question is:

• What are the opportunities and challenges associated with using generative AI in higher education?

The data for this study was generated from April 24 to November 30, 2023, using a Qualtrics online survey. Students and academics answered questions that were tailored to each participant group, which were organised around four themes: 1) awareness of GenAI (e.g. ChatGPT); 2) current use and intention to use GenAI; 3) potential of GenAI to contribute to learning and assessment; 4) affordances and challenges related to the use of GenAI; and 5) support provided for using GenAI in higher education settings. The findings are based on survey responses from 243 students and academics, with two distinct data collection periods in semesters 1 and 2 to track how the use of GenAI changed during the first year it became available. The survey invited students who were enrolled in any course or degree program at a university, and academic staff in roles such as tutors and lecturers to participate. In the results, those who were enrolled in a course or program are referred to as ‘students’ and those with teaching roles are referred to as ‘academics’. The Qualtrics platform and Excel were used to analyse the quantitative responses to Likert questions. Questions that asked for a short text response were analysed using inductive thematic coding using NVivo. Axial coding was used to find relationships between first pass codes, and to iteratively assign categories that were derived from these relationships. The key categories that emerged from this process were able to be classified as either opportunities or challenges related to the use of GenAI in higher education. This was the first study in Australia to generate data on the use of GenAI and the perspectives of students and academics in higher education during a time when GenAI was gaining momentum and new products, such as models with the capability to generate information text-to-text, text-to-image, image-image and image-text, were rapidly being released to consumers.

Students shared the purposes for which they used GenAI, including generating different outputs when assessment instructions and criteria were unclear, as a study partner, to generate revision materials and feedback on their work, to create plans, restructure writing, brainstorming, summarising literature, referencing, generating images, and students with English as a second language found the enhanced language support helpful. Academics also reported using GenAI to generate summaries and create information. Additionally, they used GenAI to develop teaching materials and exam questions, as a research tool, and to check what GenAI responses would be produced for set assessment tasks. One of the key challenges identified by respondents was the reliability of GenAI to produce accurate information and references. They found it difficult to fact check and had concerns about misinformation being reproduced. Other concerns related to the impact the techbology would have on learning and assessment, particularly in relations to people becoming reliant on technology rather than using ‘human thinking’. Ethical concerns about the difficulty detecting plagiarism were identified, as was equitable access and the possible impact on increasing the digital divide, especially for those who might not have access modern technology, tools and current information. As one academics stated, “There are so many ethical issues to work out in relation to AI, but we need to assist staff and students to understand appropriate boundaries, affordances and limitations of this technology. It will create an even bigger digital divide and inequality by placing limitations on what we want students to know and understand. It's important not to be left behind in this debate.” While there is potential for GenAI to enhance teaching and learning in higher education, critical issues remain on the impact of the technology on reliability, accessibility and ethical use in academia.

Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing Education with AI: Exploring the Transformative Potential of ChatGPT. Contemporary Educational Technology, 15(3). Bozkurt, A. (2023). Generative Artificial Intelligence (AI) Powered Conversational Educational Agents: The Inevitable Paradigm Shift. Asian Journal of Distance Education, 18(1), 198–204. Eager, B., & Brunton, R. (2023). Prompting Higher Education Towards AI-Augmented Teaching and Learning Practice. Journal of University Teaching & Learning Practice, 20(5), 1–19. https://doi.org/10.53761/1.20.5.02 Farrelly, T., & Baker, N. (2023). Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice. Education Sciences, 13(11), 1109. https://doi.org/10.3390/educsci13111109 Gallent-Torres, C., Zapata-González, A., & Ortego-Hernando, J. L. (2023). The impact of Generative Artificial Intelligence in higher education: a focus on ethics and academic integrity. Electronic Journal of Educational Research, Assessment & Evaluation / Revista Electrónica de Investigación y Evaluación Educativa, 29(2), 1–19. Ipek, Z. H., Gözüm, A. I. C., Papadakis, S., & Kallogiannakis, M. (2023). Educational applications of the ChatGPT AI system: a systematic review research. Educational Process: International Journal, 12(3), 26–55. Moya, B. A., & Eaton, S. E. (2023). Examining Recommendations for Generative Artificial Intelligence Use with Integrity from a Scholarship of Teaching and Learning Lens. Electronic Journal of Educational Research, Assessment & Evaluation / Revista Electrónica de Investigación y Evaluación Educativa, 29(2), 1–21. https://doi.org/10.30827/relieve.v29i2.29295 https://doi.org/10.30827/relieve.v29i2.29134 UNESCO. (2023). Guidance for generative AI in education and research. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000386693_eng UNESCO. (2022). Recommendation on the Ethics of Artificial Intelligence. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000381137