Special Section on Generative AI and Learning Analytics

2024-04-03

EDITORS:

AIMS & SCOPE

The deployment of Generative AI (GenAI) in education has sparked a vibrant discourse, highlighting a spectrum of opportunities and concerns (Hernández-Leo, 2023; Yan et al., 2024). Among the positive developments that many have proposed, GenAI promises to offer a more personalised learning experience, 24/7 support for diverse learners to resolve frequently asked questions, partial automation of some tasks to give educators more time with students, and instant, dialogic feedback to learners. For many (e.g., Lodge, et al. 2023) GenAI is the tipping point to accelerate the transition to more robust assessment practices. There are, of course, also valid concerns. Foremost are concerns about the integrity of conventional assessment in the age of AI (Lodge, et al. 2023; Swiecki et al., 2022) if students rely on GenAI to complete assignments, undermining the learning process (e.g., Cotton et al., 2023). There are also issues of hallucinations where GenAI provides factually incorrect outputs as well as a notable lack of transparency in explaining how their outputs are produced (Khosravi et al., 2022). Data privacy is yet another prominent issue, if students are being asked to use AI apps external to their institution, or if insecure language models have access to students’ personal information and learning records. Language and image models are, for some people, ethically compromised infrastructures, with the dominant models to date involving precarious labour from “ghost workers” (Gray & Suri, 2019), environmental impacts from computation, and litigation concerning intellectual property. Additionally, the integration of GenAI could widen the digital divide, privileging those with access to cutting-edge technology while leaving others behind.

A key strategy in harnessing GenAI’s potential to enrich education while mitigating risks is to incorporate a learning analytics lens (Khosravi et al, 2023; Yan, Martinez-Maldonado, & Gašević, 2024) to understand how students engage with GenAI-powered tools and to measure the impact of such tools on student learning experience and outcomes. This accentuates the need for robust and rigorous research at the intersection of GenAI and learning analytics.

This special section therefore invites diverse and critical viewpoints, methodologies, and findings at the intersection of Generative AI (GenAI) and learning analytics. Within the overarching theme of this section, we welcome a variety of submissions, including empirical studies, conceptual or theoretical discussions, and speculative papers, encompassing a broad array of topics outlined below.

  • Essential Elements in GenAI Analytics: Identifying crucial elements like prompts, conversational context, model parameters, retrieval methodologies, and user responses for a thorough analysis and optimization of GenAI user interactions in distinct learning contexts.
  • Key Metrics of GenAI Interactions: Determining key metrics such as interaction duration, input characters, conversational depth, and frequency to evaluate the GenAI user interaction, the degree of user agency, and ultimately the overall learning experience.
  • Understanding User Perceptions of GenAI: Developing methods to accurately gauge and analyze user experiences and perceptions specific to the use of GenAI tools in different learning settings.
  • Impact of GenAI on Learning Outcomes: Establishing methodologies to measure GenAI's effect on individual and collective learning, and integrating these insights for enhanced learning experiences.
  • Evaluating Prompt Effectiveness in GenAI: Creating frameworks to assess the impact of different prompts on user interaction, engagement, and learning outcomes in GenAI-supported learning environments.
  • GenAI in Educational Data Synthesis: Utilizing GenAI to amalgamate and analyze data across educational platforms, facilitating the creation, validation, and refinement of learning analytics models and aiding educators in making informed decisions.
  • Integrating GenAI in Learning Analytics Research: Incorporating GenAI throughout the learning analytics lifecycle, from theory to practice, to advance the research and development of learning analytics solutions.
  • Capturing AI Literacy Skills with Learning Analytics: Leveraging learning analytics methodologies (e.g., process mining and predictive modelling) to capture individuals’ AI literacy skills that are essential for them to utilize GenAI tools both effectively and ethically.
  • Empowering Learning Analytics Dashboards with GenAI: Integrating GenAI technologies within learning analytics dashboard to enhance the engagement, explanability, and utility of these dashboards in optimizing learning and teaching practices.
  • Critical perspectives on the interactions between GenAI and Learning Analytics: Arguments that challenge GenAI/LA assumptions (e.g., on epistemological, equity, ethical or political grounds), in order to propose constructive, alternative framings to guide GenAI/LA priorities.

SUBMISSION INSTRUCTIONS:

An initial submission of a 500-1000 word abstract (including title, authors, outline of the proposed article, 3-5 keywords, and key references) is optional but strongly encouraged to receive early feedback. Submit your abstract by email to the special section editors (emails above) by July 15, 2024. Full papers will undergo the standard double-blind reviewing process. Therefore, if based on your abstract, you are invited to submit a full paper, this invitation is just that and should not be taken as an indication that the final paper will be accepted. Final submissions will take place through JLA’s online submission system at http://learning-analytics.info. When submitting a paper, select the section “Special Section: Generative AI and Learning Analytics”. All submissions should follow JLA’s standard manuscript guidelines and template available on the journal website. Queries may be sent to the special section editors (emails above).

IMPORTANT DATES

  • Abstract submission emailed to the special section editors: July 15, 2024 (optional)
  • Final paper submission: August 08, 2024 (extended)
  • Decisions and comments sent to authors: September 2024
  • Revisions uploaded to the submission system: November 2024
  • Revised/final manuscripts due: December 2024
  • Publication of special section: March 2025

REFERENCES

Cotton, D., Cotton, P., & Shipway, R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International. https://doi.org/10.1080/14703297.2023.2190148

Gray, M. & Suri, S. (2019). Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. Harper & Collins

Hernández-Leo, D. (2023). ChatGPT and Generative AI in Higher Education: User-Centered Perspectives and Implications for Learning Analytics.

Khosravi, H., Buckingham Shum, S., Chen, G., Conati, C., Tsai, Y. S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 3, 100074. https://doi.org/10.1016/j.caeai.2022.100074

Khosravi, H., Viberg, O., Kovanovic, V., & Ferguson, R. (2023). Generative AI and Learning Analytics. Journal of Learning Analytics, 10(3), 1-6. https://doi.org/10.18608/jla.2023.8333 

Lodge, J. M., Howard, S., Bearman, M., Dawson, P., & Associates (2023). Assessment Reform for the Age of Artificial Intelligence. Tertiary Education Quality and Standards Agency, AUS. https://www.teqsa.gov.au/guides-resources/resources/corporate-publications/assessment-reform-age-artificial-intelligence 

Swiecki, Z., Khosravi, H., Chen, G., Martinez-Maldonado, R., Lodge, J. M., Milligan, S., Selwyn, N., & Gašević, D. (2022). Assessment in the age of artificial intelligence. Computers and Education: Artificial Intelligence, 3, 100075. https://doi.org/10.1016/j.caeai.2022.100075

Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., ... & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90-112. https://doi.org/10.1111/bjet.13370

Yan, L., Martinez-Maldonado, R., & Gašević, D. (2024). Generative Artificial Intelligence in Learning Analytics: Contextualising Opportunities and Challenges through the Learning Analytics Cycle. In Proceedings of the 14th International Conference on Learning Analytics & Knowledge (pp. 101-111). https://doi.org/10.1145/3636555.3636856