The ecosystem of education in general and Open Online Distance Education in particular is evolving and transforming due to both rapid developments in technology and changes in the socio-economic context. In order to understand these changes, we think that we are at a time when it is important to evaluate the current situation from a holistic perspective and to plan the future by looking at the past. In this context, Open Praxis journal will prioritize systematic literature review, scoping review, meta-analysis and meta-synthesis studies in its 2025 issues.
Within the scope of this call, relevant studies can be conducted on any subject within the scope of Open Praxis journal. These are:
Open education
Lifelong learning
Open and distance learning
Distance education
Online learning
Educational technology
Generative AI
Our expectation in related studies is to have a critical perspective, to analyze the past as well as future trends. Especially in systematic review studies, it is expected to present in-depth findings such as research themes instead of descriptive findings drawn from the database such as the frequency of the number of publications, the distribution of publications by country, the most cited studies.
Accepted papers will be published in regular issues in 2025. For research topics or other inquiries, please contact to Editor-in-Chief Dr. Aras Bozkurt.
The rapid and continuous development of generative artificial intelligence (GenAI) holds the power to transform and shift the traditional paradigm of teaching, learning, and training. It has shown great potential to aid in the instructional design process for personalized learning experiences. Yet, there have also been criticisms and concerns about integrating GenAI in the design, teaching, or learning processes.
We invite designers and researchers to contribute to this special issue by presenting their instructional design cases and research that utilizes GenAI in either education or training settings. Our goal is to foster conversations exploring the affordances, impacts, challenges, methodologies, and critical perspectives on incorporating GenAI technologies into various aspects of instructional/training design, including analysis, design, development, implementation, and evaluation of instructional or training interventions.
Ultimately, this endeavor aims to optimize GenAI implementations and enhance the efficacy of teaching, training, and learning practices in GenAI-supported learning environments. Specifically, we are looking for two major streams of designs or research, including (1) the application of GenAI to innovate and advance instructional design and its process and (2) the integration of GenAI, Large Language Models, or Automation for pedagogical or training innovations.
The following topics are presented for consideration; however, they do not represent an exhaustive list:
Applied instructional design cases delineating the use of GenAI in the ideation or creation of design materials (e.g., learner personas, storyboarding)
Development and validation of evaluation metrics for GenAI-generated content and design
Design and development of GenAI-supported instructional activities embedded in learning or training (e.g., scenario-based learning, collaborative learning, personalized learning)
Design cases or case studies on the affordances and benefits, challenges and obstacles, as well as ethical considerations of using GenAI in various settings
Empirical studies investigating the impacts of the GenAI design interventions on learning outcomes or training and development
Theoretical and conceptual frameworks focused on design methodologies for using GenAI in applied settings
Critical views of GenAI in applied instructional design
Other topics appropriate to GenAI, instructional design, teaching and learning, and training and development
Submission Timeline and Methods:
Article proposal within 1,000 words (excluding references) due July 22, 2024. Submit your proposal to https://forms.gle/s3fQgMYQCuMGG6gf8 If you have difficulty accessing this form, please email your proposal to Dr. Mohan Yang at mohanyang@tamu.edu and CC all guest editors. Subject line: “JAID AI proposal_last name.”
Invitations for a full manuscript will be sent to the authors by August 16, 2024
Full manuscript due December 30, 2024. Instructions to submit your full manuscript will be shared along with the article invitation
Feedback returned to authors by March 1, 2025 Revised articles due April 1, 2025 Feedback returned to authors by May 1, 2025 Revised articles due May 15, 2025 Final decisions by Jun 1, 2025 Publication in July/August, 2025
Megan Humburg, Indiana University Bloomington Haesol Bae, University at Albany, State University of New York Ingo Kollar, Universität Augsburg
As the development of AI technologies for education continues at a rapid pace (Prahani et al., 2022), the field of the Learning Sciences has a vital role to play in the conversation about how AI tools should be designed and implemented to support learning and teaching along multiple dimensions, including but not limited to sociocultural, psychological (cognitive, motivational, and emotional), and ethical layers. Issues of privacy, surveillance, and algorithmic bias present significant challenges to the design and implementation of AI tools in physical and virtual learning spaces (Akgun & Greenhow, 2021). Many students and teachers also tend to view AI systems as a “black box” in terms of how their information is used (or misused) (Greenwald et al., 2021). Research in the Learning Sciences can help us design and implement AI tools that are effective, responsible, and meaningful for analyzing learning and teaching (Luckin & Cukurova, 2019). As researchers in the Learning Sciences have explored the potential of AI, they have revealed important intersections between AI and learning, such as how AI can support ambitious learning practices (Hmelo-Silver et al., 2022), how to integrate AI with equitable design (Walsh et al., 2022), and youth perspectives and critical sensemaking around AI (Morales-Navarro et al., 2023).
The urgent need to thoughtfully integrate learning theories into the design and teaching of AI is further highlighted by the public conversations occurring in governmental, educational, and industry sectors that seek to establish shared norms for how and why AI tools are used (e.g., The White House, 2023; Council of the EU, 2023; UNESCO, 2023; Software & Industry Information Association, 2023; Ruiz et al., 2022). As we try to make sense of how increasingly powerful AI capabilities may shift practices of teaching and learning, scholars and educators alike have been calling for a deeper understanding of how AI impacts the fabric of learning environments, emphasizing the need to move beyond technical discussions to ethical and practical ones (e.g., Akgun & Greenhow, 2021; Kasneci et al., 2023). Issues such as how to center students’ voices in the AI design process (Hasse et al., 2019), address algorithmic bias and discrimination along gendered and racialized lines (Buolamwini & Gebru, 2018), and improve AI literacy for learners and teachers (Wang & Lester, 2023) are just a few of the many areas in which the Learning Sciences can utilize our theories and methods to make sense of the sociocultural, psychological, and ethical layers of how AI impacts teaching and learning.
Aims and Scope
For this special issue, we invite authors who are exploring AI technologies for teaching and learning through a humanizing lens. We are particularly interested in submissions that leverage human- centered design, participatory design, research-practice partnerships, and other approaches that deeply consider how artificial intelligence impacts learners, educators, and their communities in multifaceted ways. Our goal is to gather perspectives from the Learning Sciences that can help to inform researchers, designers, and users of AI tools of the ways in which AI can be leveraging in dignity-affirming and empowering ways.
Studies might analyze learning and teaching with AI from the perspective of sociocultural, psychological, and ethical layers of learning interactions, ideally weaving several of these layers together in their analysis. We are interested in contributions that explore not only what AI technologies can do for teaching and learning, but also how the integration of AI alters what teaching and the process of learning look like, for better or for worse. How do our notions of assessment, curricular design, and even our understanding of what it means to learn shift when learners utilize advanced generative AI as a part of the learning process? What new opportunities do AI technologies offer to learners in terms of agency and identity, and what risks do they pose for students’ dignity and privacy? What boundaries do we (or should we) create when using these tools for educational pursuits? We welcome both empirical and conceptual/theoretical submissions that explore these grey areas and grapple with both the possibilities and the pitfalls of AI technologies for teaching and learning.
Papers in the special issue could address, but are not limited to, the following themes:
? Impact of AI on Learning and Teaching Processes
? How AI shifts student learning practices, teacher pedagogical practices, and/or definitions of what it means to learn and to teach with AI
? The impact of AI tools on learners’ emotions, social connections, and community
? The impact of AI tools on student engagement
? The skills/competencies learners need to interact productively with generative AI, and how to support these skills
? The Role of a “Human-in-the-Loop” in AI tools for Learning
? Understanding what tasks and responsibilities are appropriate and effective to off-load onto AI tools for teachers and learners
? Finding meaningful balance between teacher involvement and AI-based support for specific learning environments
? Multimodal approaches to AI-driven analysis of learning that integrate human and machine approaches to understanding the learning process, or that bring human-driven and machine-driven analytic approaches into conversation with one another
? Innovative Human-Centered Designs with AI
? Designs of humanizing curriculum for AI-supported learning
? Designs for improving AI literacy for students, teachers, and / or families
? Student, teacher, and family perspectives on AI in learning spaces
? The co-design of AI tools with students and teachers as core participants
? Ethical, Moral, and Critical Considerations
? Ethical and moral dimensions of AI’s impact on learning and teaching, including but not
limited to algorithmic bias and data privacy
? Critical perspectives on the role of AI in learning and teaching
? Evaluation of AI tools and identifying risks
? AI for advancing inclusion, diversity, equity, and accessibility (IDEA) in education Papers should draw on theories and methods from the Learning Sciences, but integrations/collaborations across multiple disciplines and academic fields are welcome and encouraged. Studies may also explore a wide variety of content domains. In their submissions, we encourage authors to consider questions about who AI tools are designed for, why they are designed, and for what ends. We are interested in studies that richly explore processes of learning and teaching that are supported by AI tools or involved in building AI literacies and are looking particularly for papers that center the learning experiences (rather than centering the technology). In other words, education supported by AI or for learning about AI, rather than AI for education. Learning environments can be conceptualized broadly, and papers may address in- and/or out-of-school contexts, spanning K-12, higher education, and/or adult learning. Papers may also explore a variety of AI-driven technologies, including but not limited to generative AI, large language models, and multimodal learning analytics. Plan for soliciting and organizing articles in the special issue We invite interested authors to submit 1000-word extended abstracts that describe the key ways in which their paper will explore how we as educational researchers and designers can humanize the designs of AI technologies and teaching with AI concepts, centering the needs of learners, teachers, families, and/or communities in our decisions about where, how, and why we integrate AI into learning environments. The guest editors will select from the pool of abstracts and invite selected authors to submit full manuscripts of no more than 10,000 words and follow JLS submission guidelines. All full manuscripts will undergo a rigorous peer-review process and will receive detailed feedback from the editors, and if accepted, manuscripts will be published according to the timeline below. Abstracts should be submitted to the Qualtrics form here by June 30th, 2024. Feel free to contact the guest co-editors with any questions: Megan Humburg (mahumbur@indiana.edu) Haesol Bae (hbae4@albany.edu) Ingo Kollar (ingo.kollar@phil.uni-augsburg.de) Timeline ? Paper proposals (1000-word abstracts) due June 30th, 2024 ? Decisions on proposals/invitations to contribute full papers July 31st, 2024 ? Full manuscripts due December 31st, 2024 ? The first round of reviews completed February 28th, 2025 ? First round revisions resubmitted May 30th, 2025 ? The second round of reviews completed July 31st, 2025 ? Second round revisions resubmitted October 1st, 2025 ? Requests for minor revisions & final acceptances sent November 1st, 2025 ? Final versions of all papers due December 1st, 2025 ? Special issue published in Spring 2026
References
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Buolamwini, J., & Gebru, T. (2018, January). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency (pp. 77-91). PMLR.
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