Challenges for higher education in the era of widespread access to Generative AI

Walczak, Krzysztof; Cellary, Wojciech

doi:10.18559/ebr.2023.2.743

Abstract

The aim of this paper is to discuss the role and impact of Generative Artificial Intelligence (AI) systems in higher education. The proliferation of AI models such as GPT-4, Open Assistant and DALL-E presents a paradigm shift in information acquisition and learning. This transformation poses substantial challenges for traditional teaching approaches and the role of educators. The paper explores the advantages and potential threats of using Generative AI in education and necessary changes in curricula. It further discusses the need to foster digital literacy and the ethical use of AI. The paper’s findings are based on a survey conducted among university students exploring their usage and perception of these AI systems. Finally, recommendations for the use of AI in higher education are offered, which emphasize the need to harness AI’s potential while mitigating its risks. This discourse aims at stimulating policy and strategy development to ensure relevant and effective education in the rapidly evolving digital landscape.

References

Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein generative adversarial networks. Proceedings of the 34th International Conference on Machine Learning (PMLR), 70, 214-223.
Search in Google Scholar Back to article
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. Virtual Event. Canada.
Search in Google Scholar Back to article
Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828.
Search in Google Scholar Back to article
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33.
Search in Google Scholar Back to article
Brunner, G., Konrad, A., Wang, Y., & Wattenhofer, R. (2018). MIDI-VAE: Modeling dynamics and instrumentation of music with applications to style transfer. 19th International Society for Music Information Retrieval Conference (ISMIR), pp. 747–754. Paris, France.
Search in Google Scholar Back to article
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M. T., & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. https://doi.org/10.48550/arXiv.2303.12712
Search in Google Scholar Back to article
Chesney, R., & Citron, D. K. (2019). Deep fakes: A looming challenge for privacy, democracy, and national security. California Law Review, 107, 1753.
Search in Google Scholar Back to article
Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human preferences. Advances in Neural Information Processing Systems, 30.
Search in Google Scholar Back to article
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1, 4171–4186. Minneapolis, Minnesota. Association for Computational Linguistics.
Search in Google Scholar Back to article
Donahue, C., McAuley, J., & Puckette, M. (2018). Adversarial audio synthesis. https://doi.org/10.48550/arXiv.1802.04208
Search in Google Scholar Back to article
Engel, J., Resnick, C., Roberts, A., Dieleman, S., Norouzi, M., Eck, D., & Simonyan, K. (2017). Neural audio synthesis of musical notes with WaveNet autoencoders. Proceedings of the 34th International Conference on Machine Learning (PMLR), 70, 1068–1077.
Search in Google Scholar Back to article
Feng, Y. (2022). The rise of virtual image endorsement in visual culture context. 4th International Conference on Economic Management and Cultural Industry (ICEMCI), pp. 1622–1629. Atlantis Press.
Search in Google Scholar Back to article
Garg, N., Schiebinger, L., Jurafsky, D., & Zou, J. (2017). Word embeddings quantify 100 years of gender and ethnic stereotypes. https://doi.org/10.1073/pnas.1720347115
Search in Google Scholar Back to article
Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image style transfer using convolutional neural networks. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2414–2423. Las Vegas. U.S.A.
Search in Google Scholar Back to article
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27.
Search in Google Scholar Back to article
IFR. (2023). International Federation of Robotics. https://ifr.org/worldrobotics/
Search in Google Scholar Back to article
Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2018). Progressive growing of GANs for improved quality, stability, and variation. 6th International Conference on Learning Representations (ICLR). Vancouver. Canada.
Search in Google Scholar Back to article
Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4396–4405. Long Beach, USA. https://doi.org/10.1109/CVPR.2019.00453
Search in Google Scholar Back to article
Kingma, D.P., & Welling, M. (2014). Auto-encoding variational Bayes. https://doi.org/10.48550/arXiv.1312.6114
Search in Google Scholar Back to article
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444.
Search in Google Scholar Back to article
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. https://doi.org/10.48550/arXiv.2304.02819
Search in Google Scholar Back to article
Liu, Y., Han, T., Ma, S. Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., Wu, Z., Zhu, D., Li, X., Qiang, N., Shen, D., Liu, T., & Ge, B. (2023). Summary of ChatGPT/GPT-4 research and perspective towards the future of large language models. https://doi.org/10.48550/arXiv.2304.01852
Search in Google Scholar Back to article
Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On faithfulness and factuality in abstractive summarization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 1906–1919.
Search in Google Scholar Back to article
Nie, D., Trullo, R., Lian, J., Petitjean, C., Ruan, S., Wang, Q., & Shen, D. (2017). Medical image synthesis with context-aware generative adversarial networks. Medical Image Computing and Computer Assisted Intervention−MICCAI 2017: 20th International Conference, pp. 417–425. Quebec, Canada.
Search in Google Scholar Back to article
OpenAI. (2023). GPT-4 Technical Report. https://doi.org/10.48550/arXiv.2303.08774
Search in Google Scholar Back to article
Oord van den, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016). WaveNet: A generative model for raw audio. https://doi.org/10.48550/arXiv.1609.03499
Search in Google Scholar Back to article
Radford, A., Metz, L., & Chintala, S. (2016). Unsupervised representation learning with deep convolutional generative adversarial networks. 4th International Conference on Learning Representations (ICLR). San Juan. Puerto Rico.
Search in Google Scholar Back to article
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1−67.
Search in Google Scholar Back to article
Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645–3650. Florence. Italy.
Search in Google Scholar Back to article
Vaswani, A., Shazeer. N., Parmar N., Uszkoreit, J., Jones, J., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Search in Google Scholar Back to article
Wang, J., & Perez, L. (2017). The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Networks for Visual Recognition, 11, 1–8.
Search in Google Scholar Back to article
Zhao, J., Mathieu, M., & LeCun, Y. (2017). Energy-based generative adversarial networks. 5th International Conference on Learning Representations (ICLR). Toulon. France.
Search in Google Scholar Back to article
Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2242–2251. Venice. Italy.
Search in Google Scholar Back to article

Challenges for higher education in the era of widespread access to Generative AI

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