Navigating agentic AI: a call for reimagined academic literacies
DOI:
https://doi.org/10.47408/jldhe.vi39.1640Keywords:
agentic artificial intelligence, academic literacies, AI literacy, human-AI collaborationAbstract
The emergence of agentic artificial intelligence (AI) in higher education raises a critical question: does the move toward autonomous systems capable of planning, decision-making, and action constitute a fundamental shift, or an acceleration of challenges already posed by generative AI? This article argues that these developments require a reimagining of academic literacies, extending beyond traditional emphases on critical thinking and academic writing toward a more comprehensive conception of AI literacy. Drawing on research in AI ethics and digital pedagogy, it explores the implications of learning in partnership with increasingly autonomous systems. It examines how agentic AI disrupts established understandings of authorship, assessment, and intellectual labour, and proposes a framework centred on critical evaluation, prompt literacy, co-authorship, ethical awareness, and recognition of AI limitations. Learning developers are positioned as central actors in this transition, with a key role in shaping pedagogy, assessment, and institutional policy.
References
Ayyoub, A. M., Khlaif, Z. N., Shamali, M., Eideh, B. A., Assali, A., Hattab, M., Barham, K. A., & Bsharat, T. R. K. (2025). Advancing higher education with GenAI: factors influencing educator AI literacy. Frontiers in Education, 10, Article 1530721. https://doi.org/10.3389/feduc.2025.1530721
Balaceanu, D. (2023, February 23). Agentic AI paves the way for a new dawn in higher education. Druid AI. https://www.druidai.com/blog/agentic-ai-paves-the-way-for-a-new-dawn-in-higher-education
Belcic, I., & Stryker, C. (2025). AI agents in 2025: expectations vs. reality. IBM Think. https://www.ibm.com/think/insights/ai-agents-2025-expectations-vs-reality#:~:text=%E2%80%9CIBM%20and%20Morning%20Consult%20did,declaration%20is%20not%20without%20nuance
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, Canada, 610–623. https://doi.org/10.1145/3442188.3445922
Birhane, A. (2021). Algorithmic injustice: a relational ethics approach. Patterns, 2(2), Article 100205. https://doi.org/10.1016/j.patter.2021.100205
Bozkurt, A. (2024). GenAI et al.: cocreation, authorship, ownership, academic ethics and integrity in a time of generative AI. Open Praxis, 16(1), Article 654. https://openpraxis.org/articles/10.55982/openpraxis.16.1.654
Buckingham, D. (2015, May 21). The blanding of media literacy. David Buckingham Blog. https://davidbuckingham.net/2015/05/21/the-blanding-of-media-literacy/
Chelli, M., Descamps, J., Lavoué, V., Trojani, C., Azar, M., Deckert, M., Raynier, J-L., Clowez, G., Boileau, P., & Ruetsch-Chelli, C. (2024). Hallucination rates and reference accuracy of ChatGPT and Bard for systematic reviews: comparative analysis. Journal of Medical Internet Research, 26, Article e53164. https://doi.org/10.2196/53164
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
Floridi, L. (2023). Norms for academic writing in the era of advanced artificial intelligence. Digital Society, 2, Article 48. https://link.springer.com/article/10.1007/s44206-023-00079-7
Gonsalves, C. (2024). Addressing student non-compliance in AI use declarations: Implications for academic integrity and assessment in higher education. Assessment & Evaluation in Higher Education, 50(4), 592–606. https://doi.org/10.1080/02602938.2024.2415654
Lea, M. R., & Street, B. V. (1998). Student writing in higher education: an academic literacies approach. Studies in Higher Education, 23(2), 157–172. https://doi.org/10.1080/03075079812331380364
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, USA, 1–16. https://doi.org/10.1145/3313831.3376727
Marcus, G., & Davis, E. (2020). Rebooting AI: Building artificial intelligence we can trust. Penguin.
Mollick, E. R., & Mollick, L. (2023). Using AI to implement effective teaching strategies in classrooms: Five strategies, including prompts. The Wharton School Research Paper, 1–26 http://dx.doi.org/10.2139/ssrn.4391243
Moquin, S. (2025, May 8). Five real use cases of agentic AI in higher ed that aren’t just fancy buzzwords. Enrollify. https://www.enrollify.org/blog/5-real-use-cases-of-agentic-ai-in-higher-ed-that-arent-just-fancy-buzzwords
Newcastle University. (n.d.). Acknowledging use of AI. https://www.ncl.ac.uk/academic-skills-kit/good-academic-practice/artificial-intelligence/acknowledging/
OpenAI. (2023, January 31). New AI classifier for indicating AI-written text. https://openai.com/blog/new-ai-classifier-for-indicating-ai-written-text
Panke, S. B. (2025). GenAI literacy: What is it, and how should we teach it? Frameworks, reviews, and approaches. AACE Review. https://aace.org/review/genai-literacy-what-is-it-and-how-should-we-teach-it-frameworks-reviews-approaches/
Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
Sakana AI. (2024, August 13). The AI scientist: Towards fully automated open-ended scientific discovery. https://sakana.ai/ai-scientist/
Saxsena, C. (n.d.). Top tools and plugins to detect AI hallucinations in real-time. ISHIR Blog. https://www.ishir.com/blog/183214/top-tools-and-plugins-to-detect-ai-hallucinations-in-real-time.htm
Somasegar, S. (Host). (2025, 24 April). Reinventing recruiting: SeekOut’s Anoop Gupta on the rise of agentic AI. [Audio podcast episode]. In Founded & Funded podcast. Madrona Venture Group. https://www.madrona.com/rise-of-agentic-ai-in-recruiting-seekout-anoop-gupta/
Stauffer, J., & Gold, J. (2024, July 10). A decision tree to guide student AI use. Edutopia. https://www.edutopia.org/article/student-use-ai-helpful-framework/
PwC. (2025, June 3). The fearless future: 2025 global AI jobs barometer. https://www.pwc.com/gx/en/services/ai/ai-jobs-barometer.html
van Uffelen, N., Lauwaert, L., Coeckelbergh, M., & Kudina, O. (2024). Towards an environmental ethics of artificial intelligence. arXiv, Article 2501.10390. https://doi.org/10.48550/arXiv.2501.10390
Vertu. (2025, June 22). Is OpenAI’s AI detector accurate enough for 2025. https://vertu.com/ai-tools/openai-ai-detector-accuracy-features-user-experiences-2025/
Vespoor, K. (2024, August 20). A new AI scientist can write science papers without any human input—here’s why that’s a problem. The Conversation. https://theconversation.com/a-new-ai-scientist-can-write-science-papers-without-any-human-input-heres-why-thats-a-problem-237029
Weaver, K. D. (2024). The artificial intelligence disclosure (AID) framework: An introduction. College & Research Libraries News, 85(10), 407–411. https://doi.org/10.5860/crln.85.10.407
Waltzer, T., Pilegard, C., & Heyman, G. D. (2024). Can you spot the bot? Identifying AI-generated writing in college essays. International Journal for Educational Integrity, 20, Article 11. https://doi.org/10.1007/s40979-024-00158-3
Williams, R. T. (2024). The ethical implications of using generative chatbots in higher education. Frontiers in Education, 8, Article 1331607. https://doi.org/10.3389/feduc.2023.1331607
Wingate, U. (2006). Doing away with ‘study skills’. Teaching in Higher Education, 11(4), 457–469. https://doi.org/10.1080/13562510600874268
Xu, Z., Jain, S., & Kankanhalli, M. (2024). Hallucination is inevitable: An innate limitation of large language models. arXiv, Article 2401.11817. https://doi.org/10.48550/arXiv.2401.11817
York University. (2025). Artificial Intelligence (AI) pedagogies resources. https://www.yorku.ca/teachingcommons/resources-by-topic/artificial-intelligence-ai-pedagogical-resources/
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