UNESCO defines artificial intelligence (AI) as ‘systems which display intelligent behaviour by analysing their environment and taking actions – with some degree of autonomy – to achieve specific goals’ (UNESCO, 2019). AI has rapidly permeated various aspects in higher education (HE), promising to revolutionise learning development both from the learner and educator’s perspectives (Singh & Hiran, 2022). While AI presents numerous opportunities for enhancing educational experiences, it also raises concerns such as data privacy and algorithmic bias, which are affecting international students in the UK (Arowosegbe et al., 2024). While the potential advantages such as enhanced accessibility and personalised learning have been addressed in previous literature, this blog post aims to raise considerations of using AI in learning development in HE, highlighting the perspectives of international students.

This post focuses on an ongoing qualitative study about international students’ experiences in the UK HE under the premise of using AI technologies to facilitate their learning proactively or reactively. It includes both undergraduate and graduate levels and explores various reasons for using AI, whether voluntarily or as part of classroom practice. So far, 39 semi-structured interviews have been collected and analysed using NVivo. The participants are students from China, South Korea, Nigeria, Pakistan and Austria.

Use of AI in HE learning development

‘AI-powered language learning tools and translation services can help address the challenges of language barriers and cultural differences by providing support in understanding course materials and improving communication skills.’

AI technologies, such as adaptive learning systems (Wang et al., 2023), predictive analytics (Umer et al., 2023) and intelligent tutoring systems (Alam, 2023), offer personalised learning experiences that cater to individual learners’ needs (Holmes et al., 2019). Especially for international students, AI can be beneficial when dealing with two challenges experienced by many: language barriers and cultural differences (Luckin et al., 2016). AI-powered language learning tools and translation services can help address these challenges by providing support in understanding course materials and improving communication skills. Moreover, many international students in my study expressed that AI facilitates navigating the complexities of the education system in England, with one graduate international student commenting:

‘AI makes my life so much easier, whenever I have questions regarding academic writing, reading materials, even university’s administration, I just pop the question in ChatGPT, and I will have all the answers!’

Nevertheless, what are the downsides of applying AI in learning development in HE, from the perspective of international students in UK universities?

Concerns from international students

Despite its potential, the integration of AI in HE raises several concerns among international students. AI systems rely on vast amounts of personal data to function effectively, and the input data is collected, stored and used, which indicates the possibility of breaches of privacy and misuse of sensitive information (Binns et al., 2018). However, the awareness of this risk regarding personal data is not the consensus among international students. One of the interviewed undergraduate international students commented: ‘I never thought how ChatGPT uses my data, and no one told me about it, so there probably wouldn’t be much harm.’ This lack of awareness on data security reflects the urgent need to elevate digital literacy for international students in the era of AI development.

AI systems are only as good as the data they are trained on (West et al., 2019). So if the training data lacks diversity or contains inherent biases, such as historical or sampling biases, the AI outputs may perpetuate these biases, disadvantaging certain student groups, including international students. For example, if the training data reflects historical inequalities and prejudices, the AI system will likely perpetuate those biases. An AI system trained on historical hiring data might exhibit gender or racial biases if past hiring practices were discriminatory. This bias can manifest in various ways, such as misinterpreting cultural nuances or unfairly evaluating student performance.

Furthermore, the reliance on AI-driven learning tools might reduce human interaction, which is crucial for the holistic development of students. International students often benefit from personal interactions with faculty and peers, which help them acclimatise to a new educational environment and culture. One of the interviewed undergraduate students also revealed that ‘sometimes I feel like I don’t need to seek help from my tutors because I have Chat GPT now’. This admission raises concerns for learning developers and students that excessive dependence on AI could diminish these valuable interpersonal connections, potentially impacting student wellbeing and academic success (Selwyn, 2019).

Conclusion

AI has the potential to significantly enhance learning development in HE; however, it is not without its challenges, particularly for international students in the UK. Concerns about data privacy, algorithmic bias and reduced human interaction must be carefully managed through collective efforts, involving both institutions and those responsible for developing learning programmes and resources.

References

Alam, A. (2023). Harnessing the power of AI to create intelligent tutoring systems for enhanced classroom experience and improved learning outcomes. In G. Rajakumar, Ke-Lin Du, & Álvaro Rocha (Eds.), Intelligent Communication Technologies and Virtual Mobile Networks (pp. 571–591). Springer Nature Singapore.

Arowosegbe, A., Alqahtani, J. S., & Oyelade, T. (2024). Students’ perception of generative AI use for academic purpose in UK higher education. Preprints. Advance online publication. https://doi.org/10.20944/preprints202405.1158.v1

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.

Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.

Singh, S. V., & Hiran, K. K. (2022). The impact of AI on teaching and learning in higher education technology. Journal of Higher Education Theory and Practice, 22(13). https://doi.org/10.33423/jhetp.v22i13.5514

Umer, R., Susnjak, T., Mathrani, A., & Suriadi, L. (2023). Current stance on predictive analytics in higher education: Opportunities, challenges and future directions. Interactive Learning Environments, 31(6), 3503–3528. https://doi.org/10.1080/10494820.2021.1933542

UNESCO. (2019). Preliminary study on the ethics of artificial intelligence. https://unesdoc.unesco.org/ark:/48223/pf0000367823

Wang, S., Christensen, C., Cui, W., Tong, R., Yarnall, L., Shear, L., & Feng, M. (2023). When adaptive learning is effective learning: Comparison of an adaptive learning system to teacher-led instruction. Interactive Learning Environments, 31(2), 793–803. https://doi.org/10.1080/10494820.2020.1808794

West, S. M., Whittaker, M., & Crawford, K. (2019). Discriminating systems: Gender, race, and power in AI. AI Now Institute. https://ainowinstitute.org/publication/discriminating-systems-gender-race-and-power-in-ai-2