Concept
Question, learning context, intended decision, and feasibility
STUDY PORTFOLIO
These records cover learning, teaching, and research practice. Completed evidence records show what was reviewed and what it can support. Planned studies show methods, safeguards, and the decisions still ahead.
Portfolio status
CONTROLLED LIFECYCLE
Each record states what was reviewed or what would be tested, which learning outcomes matter, and where uncertainty remains. Completed syntheses do not become intervention findings, and planned methods do not become results before observation.
Question, learning context, intended decision, and feasibility
Design, outcomes, implementation, and analysis decisions
Scientific, education, ethics, privacy, accessibility, and security determinations
Time-stamped record, recruitment, and data collection
Results, uncertainty, limitations, artifacts, and corrections
COMPLETED RECORDS
These records synthesize public sources and methods guidance. They report bounded conclusions, not new participant outcomes.
What can controlled studies tell educators about AI tutoring, learning time, and learner experience in higher education?
Completed rapid evidence synthesis of publicly available controlled higher-education studies, with a focal randomized crossover trial and supporting research standards.
Published higher-education studies of AI tutoring and active learning. No Santaros participants, recruitment, or proprietary learner data were used.
AI tutoring conditions compared with active-learning or instructor-led teaching in the reported source studies. Design differences were retained rather than pooled.
Single focal randomized trial, one course context, intervention specificity, outcome alignment, unmeasured educator effects, and publication or selection bias.
Completed evidence brief, extraction table, source links, limitations register, and a bounded replication question list.
Which teaching and reporting practices make reproducibility teachable, assessable, and useful to scientific learners?
Completed cross-standard methods synthesis of education research, data stewardship, evidence-synthesis, and research-reporting guidance.
Public standards and guidance relevant to research-methods, data-science, and research-software teaching. No human participants or learner records were analyzed.
Outcome, provenance, uncertainty, and sharing requirements compared across standards. This record makes no causal comparison between curricula.
Standards are normative guidance rather than learner-effect estimates; terminology, discipline, licensing, and institutional requirements vary.
Completed teaching checklist, cross-standard comparison, implementation prompts, and a planned learner study specification.
PLANNED STUDIES
These prospective records are not findings. Recruitment, ethics determinations, and registration will be shown when they exist.
Can structured AI-assisted instruction improve how graduate learners generate, test, and revise scientific hypotheses?
Proposed counterbalanced learning study using matched hypothesis-generation, critique, and transfer tasks.
Graduate learners and early-career researchers in participating science programs. Disciplines, eligibility, and sample-size rationale are not yet specified.
Matched instructional tasks completed with a structured AI tutor and with instructor-authored resources, with task order counterbalanced where feasible.
Task equivalence, prior AI experience, instructor effects, disciplinary heterogeneity, rater reliability, and benchmark contamination.
Public protocol, lesson specification, scoring rubric, de-identified learner responses where permitted, and analysis code.
Can learners produce a more reproducible scientific analysis when AI assistance is embedded inside explicit provenance instruction?
Proposed blinded reproduction challenge embedded in a graduate quantitative-methods module using open datasets and containerized environments.
Graduate learners, research software trainees, and early-career analysts. Expertise thresholds and scientific domains are not yet specified.
Provenance-first AI-assisted workflow instruction compared with conventional worked examples and documentation.
Dataset familiarity, environment drift, instructor assistance, reproducer expertise, tolerance definitions, and hidden manual steps.
Teaching protocol, provenance schema, reproducibility checklist, benchmark tasks, reference containers, and model-configuration record.
Can structured AI-assisted instruction improve how learners represent consensus, uncertainty, contradiction, and evidence quality?
Proposed expert-annotated learning benchmark using source packets with supporting, null, conflicting, indirect, and lower-certainty evidence.
Graduate learners and early-career researchers in evidence-intensive disciplines. Domains and prerequisite knowledge are not yet selected.
Structured AI-assisted synthesis instruction compared with a conventional critical-appraisal worksheet using the same source packets.
Source-selection bias, expert disagreement, domain knowledge, benchmark leakage, rubric sensitivity, and construct-irrelevant writing ability.
Teaching protocol, licensed source packets, expert annotations, learner rubric, error taxonomy, and benchmark harness.
How do shared AI workspaces affect teaching, feedback, coordination, and accountability in research teams?
Proposed prospective mixed-method field study with baseline, adoption, and follow-up periods across mentored research teams.
Small research teams that include a mentor or instructor and graduate or early-career researchers. Recruitment and institutions are not yet specified.
Within-team baseline and workspace-use periods, with staggered adoption or comparison teams considered during protocol development.
Tool novelty, self-selection, group-level clustering, mentor style, confidential work, maturation, and observer effects.
Study protocol, event-log specification, mentoring observation guide, governance template, and de-identified implementation case reports.
GOVERNANCE
Sample sizes, recruitment channels, participating institutions, and review bodies are not named because they have not been verified for publication.
Before any human-participant work begins, the responsible institution or authorized review body must document the required review, exemption, waiver, consent, privacy, security, and data-sharing conditions.
CONTRIBUTE TO A STUDY
We welcome scientific collaborators who can improve tasks, outcome definitions, validity checks, and real-world constraints before a protocol is finalized.
Participation, authorship, data access, and governance are documented before research begins.