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

Authors

DOI:

https://doi.org/10.18559/ebr.2023.2.743

Keywords:

university transformation, higher education,, GPT, generative artificial intelligence, artificial intelligence

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.

Downloads

Download data is not yet available.

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.
View in Google Scholar

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. DOI: https://doi.org/10.1145/3442188.3445922
View in Google Scholar

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. DOI: https://doi.org/10.1109/TPAMI.2013.50
View in Google Scholar

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.
View in Google Scholar

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.
View in Google Scholar

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
View in Google Scholar

Chesney, R., & Citron, D. K. (2019). Deep fakes: A looming challenge for privacy, democracy, and national security. California Law Review, 107, 1753. DOI: https://doi.org/10.2139/ssrn.3213954
View in Google Scholar

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.
View in Google Scholar

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
View in Google Scholar

Linguistics: Human Language Technologies, 1, 4171–4186. Minneapolis, Minnesota. Association for Computational Linguistics.
View in Google Scholar

Donahue, C., McAuley, J., & Puckette, M. (2018). Adversarial audio synthesis. https://doi.org/10.48550/arXiv.1802.04208
View in Google Scholar

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.
View in Google Scholar

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. DOI: https://doi.org/10.2991/978-94-6463-098-5_183
View in Google Scholar

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 DOI: https://doi.org/10.1073/pnas.1720347115
View in Google Scholar

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. DOI: https://doi.org/10.1109/CVPR.2016.265
View in Google Scholar

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.
View in Google Scholar

IFR. (2023). International Federation of Robotics. https://ifr.org/worldrobotics/
View in Google Scholar

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.
View in Google Scholar

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 DOI: https://doi.org/10.1109/CVPR.2019.00453
View in Google Scholar

Kingma, D.P., & Welling, M. (2014). Auto-encoding variational Bayes. https://doi.org/10.48550/arXiv.1312.6114
View in Google Scholar

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444. DOI: https://doi.org/10.1038/nature14539
View in Google Scholar

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 DOI: https://doi.org/10.1016/j.patter.2023.100779
View in Google Scholar

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
View in Google Scholar

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. DOI: https://doi.org/10.18653/v1/2020.acl-main.173
View in Google Scholar

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. DOI: https://doi.org/10.1007/978-3-319-66179-7_48
View in Google Scholar

OpenAI. (2023). GPT-4 Technical Report. https://doi.org/10.48550/arXiv.2303.08774
View in Google Scholar

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
View in Google Scholar

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.
View in Google Scholar

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.
View in Google Scholar

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. DOI: https://doi.org/10.18653/v1/P19-1355
View in Google Scholar

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.
View in Google Scholar

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.
View in Google Scholar

Zhao, J., Mathieu, M., & LeCun, Y. (2017). Energy-based generative adversarial networks. 5th International Conference on Learning Representations (ICLR). Toulon. France.
View in Google Scholar

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. DOI: https://doi.org/10.1109/ICCV.2017.244
View in Google Scholar

Downloads

Published

2023-07-20

How to Cite

Walczak, K., & Cellary, W. (2023). Challenges for higher education in the era of widespread access to generative AI. Economics and Business Review, 9(2). https://doi.org/10.18559/ebr.2023.2.743

Issue

Section

Research article- regular issue