- Guest Editors
- Filippo Sciarrone, Universitas Mercatorum, Rome, Italy (LGE)
filippo.sciarrone AT unimercatorum.it - Pierpaolo Vittorini, Università dell’Aquila, L’Aquila, Italy (LGE)
pierpaolo.vittorini AT univaq.it - Christothea Herodotou, Open University, UK
christothea.herodotou AT open.ac.uk - Elvira Popescu, University of Craiova, RO
elvira.popescu AT gmail.com - Nuño Palmero Otero, University of Greenwich, UK
N.R.PalmeiroOtero AT greenwich.ac.uk - Chee-Kit Looi, The Education University of Hong Kong, China
cklooi AT eduhk.hk - Marcelo Milrad, Linnaeus University, Sweden
marcelo.milrad AT lnu.se
- Filippo Sciarrone, Universitas Mercatorum, Rome, Italy (LGE)
- Important dates
- Extended abstract submission: 31st August 2024
- Feedback on abstracts: 15th September 2024
- Full paper submission: 31st October 2024
- Completion of the editorial process (tentative): 28th February 2025
Rationale, Motivation, and Scope of the Special Issue
The introduction of Generative Artificial Intelligence, e.g., ChatGPT, has caused and is causing significant changes in many research fields. In the ever-evolving landscape of education, integrating Technology-Enhanced Learning (TEL) and Generative Artificial Intelligence (GAI) represents a revolutionary stride forward that opens up unprecedented possibilities.
GAI can augment TEL by providing personalized learning materials, generating new educational content, and offering adaptive learning paths. For instance, AI-generated texts can offer customized reading materials suited to a learner’s level, while AI-created simulations and visualizations can enhance understanding of complex concepts. Moreover, the integration of GAI in TEL can democratize education. Automating content creation can provide high-quality educational resources at scale, breaking down geographical and socio-economic barriers to learning.
However, this synergy has challenges. Ethical considerations, data privacy, and the potential for reinforcing biases through AI algorithms are concerns that must be addressed. Furthermore, the effective integration of GAI in TEL demands rethinking educational frameworks, pedagogies, and assessment models to harness its potential while fully mitigating its risks. Studies and tools investigating the opportunities above are starting to appear, and it is crucial to balance innovation with responsibility, ensuring that the future of education is inclusive, equitable, and human-centric.
In summary, the relationship between TEL and GAI is a dynamic and evolving research area poised to redefine educational paradigms.
The special issue comes one year after the groundbreaking releases of the most significant LLMs (e.g., GPT-4 and LLaMA2) and, therefore, after sufficient time for researchers to conduct thorough studies producing solid results. Thus, this special issue aims to collect papers discussing the effectiveness of using GAI in supporting students through textual and visual feedback, from the manifold perspective of the learners’ experience, engagement, and outcomes. In summary, novel technological and methodological approaches to TEL, supported by GAI, whose educational impacts were measured with sound experiments, are desirable contributions to the special issue. Finally, manuscripts showing a positive effect of GAI on learners with special educational needs are particularly welcome.
Accordingly, this special issue targets educators, researchers, policymakers, and technology developers interested in proposing works on and discussing the impact of technologies based on GAI on learning and teaching methodologies.
Main topics (not limited to)
- GAI applied to learning and e-learning
- GAI to support learners with special educational needs
- GAI in the design and implementation of adaptive and recommending e-learning systems
- GAI and Immersive Learning Technologies
- Learning analytics to support GAI
- Teacher vs Peer vs GAI assessment
Main macro-areas
- Diverse Perspectives: Papers ranging from theoretical frameworks and empirical studies to case reports and reviews, offering a holistic view of the current state and future potential of Generative AI in education.
- Innovative Applications: Exploring how AI-generated content can be used in various educational settings, from primary schools to higher education, including vocational training and lifelong learning.
- Ethical and Societal Implications: A critical examination of the ethical considerations, data privacy issues, and the potential for bias in AI algorithms, ensuring a balanced and responsible approach to technology adoption in educational contexts.
- Future Directions: Discussion of emerging trends, potential research areas, and the future landscape of TEL as influenced by Generative AI, based on solid empirical results, providing a roadmap for educators and technologists.
- Practical Insights: Contributions from educators who have integrated Generative AI into their teaching, sharing real-world experiences, challenges faced, and practical solutions.
Editorial process
The editorial process is as follows (see also the important dates above):
- Extended abstract submission
- Feedback on abstracts (i.e., proceed to full paper submission or not)
- Full paper submission
- First (and potentially second) round of reviews
- Final notification (accept/reject)
- Publication
Extended abstract submission
Potential authors are invited to use the EasyChair system to submit their extended abstracts. The extended abstract should summarise in no more than 1000 words:
- the topic under study and its connection with the special issue;
- the research question(s);
- what is already known about these questions;
- the rationale and goals for your research (e.g., why is it important to address these questions? Are you filling a gap in previous research?);
- the research and/or analytical methods;
- the main findings and results;
- the significance/implications of your findings.
More information
https://link.springer.com/collections/ccbhgbfbab
https://vittorini.univaq.it/?page_id=3922
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