JIOS Special Issue | Actionable Learning Analytics and AI-Supported Learning Design

Rationale

Why this special issue matters

This special issue explores the interplay between meaningful learning design, actionable learning analytics, and artificial intelligence in educational environments. As educational systems become increasingly data-rich and AI-enabled, the central challenge is no longer simply collecting data, but transforming data and AI capabilities into meaningful, actionable support for educators, students, and institutions. This especially refers to finding evidence for the effectiveness of educational interventions and, as a consequence, supporting educational decision-making.

Over the past decades, learning design has evolved through three major waves:

From conceptual approaches to evidence-informed design

Initial approaches focused primarily on pedagogical concepts, instructional models, and teacher-led curriculum planning (Mangaroska & Giannakos, 2019). Subsequent studies have used these conceptual approaches to empirically test and validate how educators are using learning design, providing support that learning design decisions (in)directly influence students’ affect, behaviour, and cognition (Albuquerque et al., 2025; Drugova et al., 2024).

From course-level to programme-level learning design

The focus progressively expanded from learning design decisions made within (elements of) individual courses toward programme coherence, curriculum alignment, and institutional learning ecosystems (Divjak et al., 2026).

From data-driven to AI-supported learning design and analytics

Learning analytics introduced new possibilities for evidence-informed educational decision-making, while recent advances in artificial intelligence are reshaping how educational data are interpreted, operationalized, and transformed into recommendations and interventions (Divjak et al., 2025; Giannakos et al., 2025; Rienties et al., 2026; Xavier et al., 2025).

At the centre of these developments is the emergence of actionable learning analytics: analytics that not only describe or predict learning processes, but actively support timely and effective pedagogical decisions and interventions (e.g., Tempelaar et al. 2016).

Critical questions include

Actionable learning analytics and AI-supported educational decision-making

01 How can learning analytics become genuinely actionable for educators, programme leaders, and students?

02 Which forms of AI support improve or hamper educational decision-making, and under what conditions?

03 How does AI reshape learning design processes, curriculum planning, assessment practices, and feedback mechanisms?

04 To what extent can AI-supported systems help educators move from reactive to proactive learning design?

05 How can AI support interpretation of learning analytics while preserving pedagogical autonomy and professional judgment?

06 How does AI reshape learning design and educational decision-making?

Possible topics

Possible topics include, but are not limited to:

Actionable learning analytics

Evidence-based interventions in education

AI-supported learning design

Learning analytics for study programme and curriculum development

Human-AI collaboration in the curriculum development

Explainable and trustworthy AI in learning design

Student agency and student-centred and participatory learning analytics

Equity, inclusion, and bias in AI-supported educational decision-making

Evaluation methodologies for learning analytics (including AI-supported interventions)

Organizational and institutional implications of AI in education based on learning analytics insights

Human-AI collaboration in educational decision-making

Evaluation methodologies for AI-supported educational interventions

Equity, bias, and inclusion issues in learning analytics

Submission path

How to publish your paper

01 Read the call

Confirm fit with the special issue scope and contribution types.

02 Prepare manuscript

Follow JIOS author guidelines and journal formatting requirements.

03 Submit via JIOS

Use the journal platform and select the special issue during submission.

04 Peer review

Move through editorial screening, review, revision, and publication.

Review and acceptance

For the JIOS Special Issue, all standard rules and procedures related to manuscript submission, editorial screening, peer review, acceptance, and publication apply in the same manner as for regular issues. All submissions are subject to an initial editorial screening.
Manuscripts that do not meet the journal’s scope, quality standards, or submission requirements may be returned without external peer review.

All submissions undergo a rigorous double-blind peer review process involving at least two independent reviewers. Manuscripts are evaluated based on originality, scientific quality, methodological rigor, and relevance to the field of information systems in organizational, industrial, or societal contexts. The journal adheres to internationally recognized standards of publication ethics and follows the guidelines of the Committee on Publication Ethics (COPE). All manuscripts are screened for plagiarism prior to peer review.
More information about the peer review process is available at: https://jios.foi.hr/index.php/jios/peer-review-process

JIOS supports open access publishing and does not charge authors any submission or publication fees. The journal encourages high-quality original research, as well as systematic literature reviews and meta-analyses conducted in accordance with established methodological standards such as PRISMA.