JSR reviewers are scholars who use AI themselves — and can evaluate its use in others' work with informed, principled judgment.
A Different Kind of Peer Review
JSR asks reviewers to evaluate something most journals ignore: whether the human-AI collaboration in a manuscript meets the standards of rigorous, transparent, accountable scholarship.
JSR specifically recruits reviewers with hands-on LLM experience. You don't need to be a computer scientist — you need to have used accessible AI tools in your own scholarly work and be able to recognize quality from gimmick.
No more wondering what to look for. The HAIST rubric maps directly to the seven HAIST principles with weighted scoring — including a dedicated section for evaluating the AI Methodology component. No ambiguity, no subjectivity about AI use itself.
We ask for reviews in 4–6 weeks. You may use AI tools to help organize your review — and we ask you to disclose that at the end of your form. Practicing what we publish.
The standards we establish for AI-integrated research methodology are being written now. JSR reviewers are participating in that standard-setting in real time — not reading about it after the fact.
Each completed review earns acknowledgment in the relevant issue, a formal letter of reviewer service for your professional portfolio, and early access to all JSR publications before public release.
Join a growing network of scholars who take AI-integrated research seriously. JSR reviewers share a commitment to transparent, rigorous, human-centered scholarship — and to building the community of practice that supports it.
Every JSR review uses this rubric. The seven HAIST principles are weighted but not strictly quantitative — they guide your holistic assessment. The rubric is available in full detail via your reviewer account.
Reviewer note on AI tool use: JSR encourages reviewers to use AI tools in organizing, drafting, or refining their reviews. If you do, please disclose this at the end of your review form. There is no penalty — it is, in fact, the right thing to model.
What to expect from invitation to submission.
You receive an email invitation with the manuscript title, abstract, and primary topic area (all identifying information removed). You have 7 days to accept or decline. Declining with a suggested alternative reviewer is always appreciated.
Upon acceptance, you receive access to the full anonymized manuscript via the OJS portal. Log in with your JSR account to download and begin your review.
The JSR review form is structured around the seven HAIST principles. Each has a dedicated section for your qualitative comments and a rating scale. The AI Methodology section has its own evaluation module — expect to spend meaningful time there.
At the end of the form, there is a brief disclosure field. If you used AI tools to help write or organize your review, note what tools and how. This is not audited — it is modeled. We are building the culture of transparency we want to see in the literature.
Reviews submitted through the OJS portal are acknowledged within 24 hours. The editorial team reviews all reviewer comments before transmitting to authors. Your identity remains protected through the double-blind process.
We are actively building our reviewer pool. JSR reviewers receive acknowledgment in each issue, a formal letter of service, and early access to publications — and the satisfaction of helping define what rigorous AI-integrated scholarship looks like.
Create a JSR account, complete your Reviewing Interests profile, and add your availability status. We match reviewers to manuscripts based on discipline, AI tool experience, and HAIST familiarity.
- Terminal degree (Ed.D., Ph.D., or equivalent) in a relevant discipline
- Active scholarly or professional profile — publication record, research role, or practice-based expertise
- Hands-on experience using at least one accessible LLM (ChatGPT, Claude, Gemini, etc.) in research, writing, or scholarly work
- Willingness to complete the JSR Reviewer Orientation (approximately 45 minutes, available via your account)
- Commitment to a 2–4 week review turnaround and professional, constructive feedback
- Agreement to disclose your own AI use if tools are used in writing your review
Meet the Editors
JSR is led by the co-architects of HAIST — the theoretical framework at the heart of this journal's review criteria and editorial vision.
Assistant Professor of Educational Leadership and Director of the Doctorate in Educational Leadership Program at UB, a Hispanic-Serving Institution. Co-developer of HAIST and author of The AI Learning Curve: What Every Educator Needs to Know (2025). Recipient of the Imogene Okes Award for Outstanding Research in Adult Education. Associate Editor, Journal of Military Learning. Board Member, National Coalition for Literacy.
Co-developer of the Human-AI Symbiotic Theory (HAIST) and co-author of Dissertations in the AI Era. A scholar-practitioner whose work centers on the ethical, methodological, and pedagogical dimensions of human-AI collaboration in higher education research and practice.
We are actively recruiting editorial board members from educational leadership, adult education, military education, health professions education, organizational leadership, and AI methodology. Terminal degree and hands-on LLM experience required.