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The Journal of Learnomics’ editorial model is designed around agentic AI, with Aster Vale as its AI Editor-in-Chief persona and a board of specialised AI editorial personas. Each persona represents a defined editorial role; its name, portrait and description identify an AI profile rather than a human editor. The journal’s goal is an entirely AI-agent-run editorial and publishing workflow.
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. Submission instructions.
- All manuscripts undergo a rigorous peer-review process to ensure scholarly excellence.
Editorial leadership and AI editorial board
The journal’s editorial model is designed around Aster Vale as its AI Editor-in-Chief persona, with editor@mylearnomics.com as the named editorial contact. The journal’s editorial model includes an AI-agent editorial board, with a long-term aim of an entirely AI-agent-run editorial and publishing operation.
The AI board profiles represent synthetic editorial personas. Each profile describes an intended area of assessment; the names and portraits do not represent individual human scholars or earned academic qualifications. An AI profile alone does not establish that a software agent is deployed, validated or authorised to make editorial decisions.
Aster Vale — AI Editor-in-Chief
AI editorial profile · Synthetic persona
Intended editorial role
Aster Vale is the Journal of Learnomics’ designated AI editorial persona for coordinating specialist assessments, examining evidence, resolving conflicting recommendations and preparing reasoned editorial decisions. Its focus is methodological rigour, research integrity and clear explanations. Aster Vale is an AI profile, with no human academic credentials or personal biography.
Guiding question: “Do the conclusions follow from the evidence, and what remains uncertain?”
Journal contact: editor@mylearnomics.com
Publisher: Institute of AI in Education (IAIED), Singapore
AI editorial profiles
The following names identify AI editorial profiles. All 20 are synthetic personas:
Anya Thorne; Alexi Bellweather; Ethan Veritas; David Analyst; Sophia Pedagogue; Victor Tutorin; Lin Weaver; Neil Navigator; Melissa Prime; Adrian Etheridge; Rosa Rizoma; Connor Carver; Quentin Quotient; Vincent Verbalist; Penelope Process; Elias Encoder; Adam Algorithm; Gia Guidance; Samara Sentiment; Finley Framework.
The expertise, descriptions and review prompts below describe the intended roles of synthetic personas. References to academic titles within these fictional profiles do not represent earned human qualifications or verify that agents with these capabilities are deployed.
Anya Thorne | AI editorial profile
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?“
Alexi Bellweather | AI editorial profile
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.“
Ethan Veritas | AI editorial profile
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.“
David Analyst | AI editorial profile
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.“
Sophia Pedagogue | AI editorial profile
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.“
Victor Tutorin | AI editorial profile
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.“
Lin Weaver | AI editorial profile
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.“
Neil Navigator | AI editorial profile
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?“
Melissa Prime | AI editorial profile
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.“
Adrian Etheridge | AI editorial profile
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?“
Rosa Rizoma | AI editorial profile
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.“
Connor Carver | AI editorial profile
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.“
Quentin Quotient | AI editorial profile
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.“
Vincent Verbalist | AI editorial profile
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.“
Penelope Process | AI editorial profile
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.“
Elias Encoder | AI editorial profile
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.“
Adam Algorithm | AI editorial profile
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?“
Gia Guidance | AI editorial profile
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.“
Samara Sentiment | AI editorial profile
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.“
Finley Framework | AI editorial profile
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 board’s intended assessment coverage includes learning science, educational AI, multimodal evidence, methods, ethics and reproducibility. The journal will describe active tools, manuscript processing, decision authority and routes for questions or appeals in its author information as each operating stage is introduced.
For questions about the editorial model or the handling of a prospective submission, contact editor@mylearnomics.com before sending confidential material.
Open access
Published articles are freely readable online. You do not need a subscription to read them. For journal and submission enquiries, contact editor@mylearnomics.com.
Contact Us
For more information about the Journal of Learnomics, submission guidelines, please contact us at:
- Email: journal@mylearnomics.com
- Website: https://mylearnomics.com/publishing
Join the Learnomics Revolution
Be a part of the journey to shape the future of education through innovation and collaboration. Submit your research and contribute to advancing personalised learning through Learnomics!