Call for papers SPECIAL ISSUE: Artificial intelligence in the evaluation and personalization of learning

05-05-2023

call_for_papers_1600_AI.jpg

Editors of the Special Issue: 

 

Keywords: personalization of learning, student assessment, individualized learning paths, intelligent tutoring systems, adaptive learning.

Publication date of the issue: September 2024

Presentation

In recent times, new expectations have been generated about the use of Artificial Intelligence in Education (AIED) especially due to the achievements of Artificial Intelligence (AI) in general, the applications that have been put at the service of the population, the greater public awareness on the topic, and the effects of COVID on teaching and learning processes (Du Boulay et al., 2023). Despite the relevance and currentness of the topic, research on AIED has a long history that dates back to the 1970s with the promise that intelligent tutors would have the potential to change education through personalization (Woolf, 2007).

Together with the assessment of learning in a digital world (Du Boulay et al., 2023), the personalization of learning is one of the most highlighted opportunities of the IED (UNESCO, 2019; Zawacki-Richter et al., 2019). In an educational context, it is understood as adaptation that combines a student's goals, interests, and competencies with a continuous process of adjustment as previous conditions and context change (Bulger, 2016). 

It is a process that is not dependent on the use of technology, although there is a strong association if we analyze contemporary trends in educational technology research. Terms such as learning analytics, machine learning or data-driven teaching are frequently related to the personalization of learning (Scanlon, 2011; Serrano, 2021).

For decades, technological systems of personalization of learning have tried to attend to individual differences with the lowest possible economic cost and in a crowded educational system. During the twentieth century, initiatives, theories and technologies were designed (teaching program, computer-aided teaching, adaptive learning, learning analytics...). ICAI (Intelligent Computer Assisted Instruction) systems emerged that made AI the possibility of making decisions, learning from a large database that is enriched by the training that the user does after using the technology (Bartolomé & Lindín, 2019).

In the XXI century, we talk about adaptive learning environments powered by TEALE technology (for its acronym in English) that have been criticized from the pedagogical sector due to the lack of evidence that confirms its efficiency and that go beyond the validation of the technological product (Bartolomé, et al., 2018).

Technological developments in personalizing learning are more promising than realistic. There is little knowledge about its quality, its educational usefulness and its generalization to multiple contexts. Most focus on providing recommendations and measuring the time a user spends on different resources. The usual, and almost single, purpose is the improvement of academic performance. They leave behind aspects that are widely studied and that we do know that it improves the quality of the teaching and learning process, such as: self-regulation of learning, self-determined behaviour (Deci & Ryan, 2000), the emotional well-being of the student or the social dimension of learning (Bulger, 2016).

Intelligent tutors are called to be the adaptive systems that offer the greatest potential. They aim to become a proactive learning guide to go beyond the scope of assistant. They seek to guide and inspire beyond a decision tree that provides limited answers. However, they are still considered more of a promise than a reality (Bulger, 2016).  In this sense, the latest developments in AI, added to accessibility and integration possibilities with other technological tools and platforms, pose a new scenario in the educational field that deserves to be studied.

With this call we intend to present those investigations that, from educational technology, study how the personalization of learning guided by AI can contribute to designing teaching models and learning situations in formal and non-formal educational contexts.

Our perspective is to bet on an integration of the AIED that assists and guides the student and the educator in decision-making to design individualized learning itineraries (Salinas & De Benito, 2020) and improve evaluation models (González-Calatayud et al., 2022; Ouyang et al., 2022) taking into account the complexity of education.

 

Topics or lines of interest
  • Adaptive learning environments powered by technology.
  • AI in learning assessment.
  • Influence of AI in the design of teaching-learning situations.
  • Individualized AI-assisted learning paths
  • Intelligent tutoring systems. 

 

References:

Bartolomé, A., Castañeda, L., & Adell, J. (2018). Personalisation in educational technology: the absence of underlying pedagogies. International Journal of Educational Technology in Higher Education, 14. https://doi.org/10.1186/s41239-018-0095-0

Bartolomé, A., & Lindín, C. (2019). Posibilidades del Blockchain en Educación. Education in the Knowledge Society, 19 (4). https://doi.org/10.14201/eks20181948193 

Bulger, M. (2016). Personalized Learning: The Conversations We're Not Having. Data & Society. https://datasociety.net/library/personalized-learning-the-conversations-were-not-having/

Du Boulay, B., Mitrovic, A. y Yacef, K. (2023). Introduction to the Handbook of Artificial Intelligence in Education. B. Du Boulay, A. Mitrovic y K, Yacef, Handbook of Artificial Intelligence in Education. Edward Elgar Publishing

González-Calatayud, V., Prendes-Espinosa P., & Roig-Vila, R. (2021). Artificial Intelligence for Student Assessment: A Systematic Review. Applied Sciences 11 (12). https://doi.org/10.3390/app11125467

Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27. https://doi.org/10.1007/s10639-022-10925-9

Salinas, J., & De Benito, B. (2020). Construction of personalized learning pathways through mixed methods. Comunicar, 65. https://doi.org/10.3916/C65-2020-03

Scanlon, E. (2021). Educational Technology Research: Contexts, Complexity and Challenges. Journal of Interactive Media in Education ,1. https://doi.org/10.5334/jime.580

Serrano, J.L. (2021). Qué debes saber antes de usar tecnología para enseñar y aprender. Blog eduHacking. https://joseluisserrano.net/tecnologia-educativa/

UNESCO (2019). Beijing Consensus on Artificial Intelligence and Education. https://unesdoc.unesco.org/ark:/48223/pf0000368303

Zawacki-Richter, O., Marín, V.I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators?. International Journal of Educational Technology in Higher Education, 16, (39) https://doi.org/10.1186/s41239-019-0171-0

Woolf, B.P. (2007). Building Intelligent Interactive Tutors. Student-centered strategies for revolutionizing e-learning. Elsevier. https://doi.org/10.1016/B978-0-12-373594-2.X0001-9

 

Important dates:
  • Open the submission period: September 2023.
  • Submission Deadline:  March 31th, 2024.
  • Publication of the special issue: September, 2024.

 

About editors

José Luis Serrano; jl.serranosanchez@um.es
University of Murcia, Spain
 

Senior lecturer/Associate Professor (tenured) at the University of Murcia. Member of the Educational Technology Research Group at the University of Murcia. Executive Editor RiiTE Interuniversity Journal of Research in Educational Technology. His research focuses on how technology can support and guide teaching and learning processes and the development of computational thinking in education. Author of the education blog and podcast eduHacking (www.joseluisserrano.net).

Juan Moreno-García; juan.moreno@uib.es
University de les Balearic Islands, Spain

Professor of the Department of Applied Pedagogy and Educational Psychology at the University of the Balearic Islands. Researcher at the Educational Technology Group. Associate editor of Edutec, Electronic Journal of Educational Technology.  His lines of work focus on higher education and educational technology. In particular, in the design and implementation of personalised learning environments, the creation and use of open educational resources, knowledge management and virtual communities of practice.