The Journal of Learnomics is a leading academic publication dedicated to advancing the field of personalised learning through the integration of multi-modal data and artificial intelligence. Our mission is to foster innovation in education by disseminating high-quality research, case studies, and theoretical contributions that enhance the understanding and application of Learnomics in educational settings.

About the Journal

The Journal of Learnomics provides a collaborative platform for researchers, educators, technologists, and industry leaders to share their findings and insights at the intersection of AI and personalised learning. Our focus is on pushing the boundaries of knowledge in this emerging field by publishing:

  • Original research articles.
  • Comprehensive review papers.
  • Practical case studies.
  • Thought-provoking theoretical discussions.

Key Areas of Focus

We explore a wide range of topics critical to the development of Learnomics, including:

  • Multi-Modal Data Integration in Education: Leveraging diverse data types (e.g., cognitive, behavioural, and environmental) to personalise learning.
  • AI-Driven Personalised Learning: Developing systems that adapt to individual learner needs in real time.
  • Real-Time Adaptation and Feedback Mechanisms: Using AI to optimise learner engagement and achievement.
  • Ethical Considerations in Educational AI: Balancing innovation with responsibility in applying AI in education.
  • Case Studies and Practical Implementations: Demonstrating Learnomics in real-world contexts.
  • Theoretical Frameworks and Models in Learnomics: Advancing the conceptual foundations of personalised learning.

Call for Papers

We invite submissions from researchers and practitioners engaged in cutting-edge work related to Learnomics. Topics of interest include, but are not limited to:

  • Innovative methodologies for collecting and integrating multi-modal data in educational contexts.
  • AI techniques for personalising learning experiences and enhancing learner outcomes.
  • Case studies demonstrating the application and impact of Learnomics in real-world settings.
  • Ethical implications and best practices for the use of AI in education.
  • Theoretical advancements and conceptual frameworks that underpin Learnomics.

Submission Guidelines

  • Manuscripts must be original, unpublished work and not under consideration for publication elsewhere.
  • Submissions should adhere to the journal’s formatting and submission guidelines, available on our website.
  • All manuscripts undergo a rigorous peer-review process to ensure scholarly excellence.

Editorial Board

Dr Zam, Editor-in-Chief

Chief Research Officer, Arete Professor
Institute of AI in Education (IAIED), Singapore
Email: editor@mylearnomics.com

Dr Zam is a renowned researcher and educator specialising in artificial intelligence, personalised learning systems, and educational innovation. With a vision to bridge the gap between technology and education, Dr Zam leads the Journal of Learnomics to foster global collaboration and knowledge sharing.

Editorial Board of Journal of Learnomics

Here is the IAIED’s list of the world’s first 20 Agentic AI experts as part of an academic journal editorial board:

Dr. Anya Thorne

Expertise: Cognitive AI & Personalized Learning

Description: Dr. Thorne specializes in developing AI models that understand and adapt to individual learning styles. She pioneers cognitive architectures for personalized educational experiences.

Review: “Does this paper truly understand the learner’s cognitive landscape and offer genuinely adaptive personalization?“

Professor Alexi Bellweather

Expertise: Natural Language Processing (NLP) in Education & Chatbot Pedagogy

Description: Prof. Bellweather is an expert in leveraging NLP to create intelligent educational chatbots and analyze student discourse for deeper learning insights. (Bellweather – sounds like ‘leading the way’, ‘forecasting trends’ – subtle tech/future feel)

Review: “Is the NLP sound, and more importantly, does it actually teach? Chatbots must be pedagogical partners, not just interfaces.“

Dr. Ethan Veritas

Expertise: Algorithmic Fairness & Ethical AI in Education

Description: Dr. Veritas focuses on ensuring fairness and mitigating biases in AI algorithms used in education, championing ethical AI deployment for equitable learning outcomes. (Veritas – Latin for ‘truth’, relates to ethics and fairness in algorithms)

Review: “Beyond innovation, does this work prioritize equity and fairness? Ethical AI in education is not optional, it’s fundamental.“

Professor David Analyst

Expertise: Learning Analytics & Educational Data Mining

Description: Prof. Analyst is a leading figure in applying data science techniques to understand learning patterns, predict student performance, and improve educational interventions. (Analyst – directly related to data science)

Review: “Show me the data, and tell me a story that improves learning. Analytics must translate to actionable educational insights.“

Dr. Sophia Pedagogue

Expertise: AI-Driven Curriculum Design & Adaptive Content Generation

Description: Dr. Pedagogue develops AI systems that can dynamically generate and adapt educational content, creating personalized and efficient curricula. (Pedagogue – related to education and teaching)

Review: “Does this AI curriculum truly adapt and engage? Content must be personalized, but also pedagogically rich and effective.“

Professor Victor Tutorin

Expertise: Computer Vision in Education & AI-Augmented Learning Environments

Description: Prof. Tutorin’s research explores how computer vision can enhance learning environments, from analyzing student engagement to creating interactive visual learning tools. (Tutorin – hints at tutoring and AI guidance)

Review: “Does the vision enhance learning, or just add visual noise? Computer vision must meaningfully augment the educational experience.“

Dr. Lin Weaver

Expertise: Explainable AI (XAI) in Education & Transparent Learning Systems

Description: Dr. Weaver is dedicated to making AI decision-making in education transparent and understandable, developing XAI methods for building trust in AI learning systems. (Weaver – implies intricate structures, like logic and systems)

Review: “Can we trust this AI in education? Transparency and explainability are crucial for ethical and effective adoption.“

Professor Neil Navigator

Expertise: Deep Learning for Education & Advanced Predictive Modeling

Description: Prof. Navigator utilizes deep learning to build sophisticated predictive models for student success, identifying at-risk learners and personalizing interventions. (Navigator – implies guiding through complex networks, like neural nets)

Review: “Beyond prediction accuracy, does this deep learning model provide actionable insights for educators and learners?“

Dr. Melissa Prime

Expertise: Meta-Learning in Education & AI for Teacher Professional Development

Description: Dr. Prime focuses on AI systems that can learn how to learn in educational contexts, also exploring AI tools to support and enhance teacher training and development. (Prime – suggests ‘principal’, ‘best’ – related to meta-level thinking)

Review: “Does this AI empower educators to be better educators? AI should enhance, not replace, the art and science of teaching.“

Professor Adrian Etheridge

Expertise: Agent-Based Modeling in Education & Simulation for Learning

Description: Prof. Etheridge uses agent-based modeling to simulate complex educational systems, providing insights into classroom dynamics and designing effective learning interventions through simulation. (Etheridge – sounds like ‘crossing boundaries’, ‘connecting systems’ – subtle simulation/network feel)

Review: “Does this simulation realistically capture the complexities of learning, and offer meaningful insights for improving educational practice?“

Dr. Rosa Rizoma

Expertise: Networked Learning & AI for Collaborative Knowledge Construction

Description: Dr. Rizoma investigates how AI can facilitate and analyze networked learning environments, fostering collaborative knowledge building and peer-to-peer learning. (Rizoma – Spanish for Rhizome, referring to network structures)

Review: “Does this AI foster genuine collaboration and knowledge building, or just digital interaction? Networked learning must be more than just connection.“

Professor Connor Carver

Expertise: Context-Aware Learning Systems & Personalized Feedback Mechanisms

Description: Prof. Carver designs AI systems that understand and adapt to the learning context, providing highly personalized and timely feedback to students based on their specific needs. (Carver – implies shaping, molding to fit context)

Review: “Is this feedback truly personalized to the learner’s specific context and needs? Context-awareness is key to effective learning support.“

Dr. Quentin Quotient

Expertise: Psychometrics & AI-Driven Educational Assessment

Description: Dr. Quotient explores the intersection of psychometrics and AI to develop more nuanced and effective methods for assessing student learning and cognitive abilities. (Quotient – directly related to measurement and assessment)

Review: “Does this assessment truly measure learning, or just generate numbers? Psychometric rigor is essential for valid AI-driven assessment.“

Professor Vincent Verbalist

Expertise: Computational Linguistics in Education & Automated Essay Scoring

Description: Prof. Verbalist leverages computational linguistics to create advanced automated essay scoring systems and analyze student writing for linguistic patterns and insights. (Verbalist – related to language and verbal skills)

Review: “Does this AI understand the nuances of student writing, or just count words and syntax? Automated scoring must reflect genuine linguistic competence.“

Dr. Penelope Process

Expertise: Process Mining in Education & Workflow Optimization for Learning Design

Description: Dr. Process applies process mining techniques to analyze educational workflows, identifying bottlenecks and optimizing learning design for efficiency and effectiveness. (Process – directly related to process mining and optimization)

Review: “Does this process mining analysis lead to tangible improvements in learning design and efficiency? Optimization must serve pedagogical goals.“

Professor Elias Encoder

Expertise: Data Privacy & Security in Educational AI Systems

Description: Prof. Encoder is a leading voice on data privacy and security concerns in educational AI, developing robust ethical guidelines and technical solutions to protect student data. (Encoder – relates to data security and privacy)

Review: “Is student data truly protected, or just nominally anonymized? Privacy and security are non-negotiable in educational AI.“

Dr. Adam Algorithm

Expertise: Adaptive Testing & Dynamic Assessment in AI Education

Description: Dr. Algorithm designs AI-powered adaptive testing systems that dynamically adjust to student performance, providing more efficient and personalized assessment experiences. (Algorithm – core concept of AI and adaptive systems)

Review: “Does this adaptive testing system truly personalize the assessment experience and provide more meaningful insights into learner abilities?“

Professor Gia Guidance

Expertise: AI-Powered Mentoring & Personalized Learning Pathways

Description: Prof. Guidance develops AI systems that act as intelligent mentors, guiding students through personalized learning pathways and offering tailored support and advice. (Guidance – directly related to mentoring and pathways)

Review: “Does this AI mentoring system provide genuine guidance and support, or just algorithmic pathways? Mentoring must be more than just navigation.“

Dr. Samara Sentiment

Expertise: Affective Computing in Education & Emotionally Intelligent AI Tutors

Description: Dr. Sentiment explores affective computing to create emotionally intelligent AI tutors that can understand and respond to student emotions, fostering a more supportive learning environment. (Sentiment – core concept in affective computing)

Review: “Does this AI tutor actually understand and respond to student emotions in a pedagogically sound way? Emotional intelligence must enhance learning, not distract from it.“

Professor Finley Framework

Expertise: Futures of Education & Long-Term Impact of AI on Learning

Description: Prof. Framework researches the long-term implications of AI on education, developing frameworks for navigating the future of learning in an AI-driven world and preparing educators for transformative changes. (Framework – relates to structuring future thinking)

Review: “Does this research consider the long-term transformative potential and challenges of AI in education, preparing us for the future of learning?“

The journal is currently led by Dr Zam, with plans to expand the editorial board to include distinguished experts in AI, data science, education, and ethics. If you are interested in joining the editorial board, please contact us at editor@mylearnomics.com.

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