Agentic education · Learning science

Actionable companion · Feedback & scaffolding

Learning with agency: Ethical engagement with artificial intelligence in faculty research education: evaluation of the New Knowledge Help (NKH) approach

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Adult & professional learning. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Background Artificial intelligence (AI) offers new opportunities in healthcare research; however, faculty often lack structured training on its effective and ethical use. Existing faculty development initiatives frequently emphasise tool awareness rather than supporting ethical, reflective, and process-oriented engagement with AI. The New Knowledge Help (NKH) approach was developed as a pedagogical framework to support ethical engagement with AI in faculty research education and was operationalised using instructional design principles. This study evaluated the NKH approach using Kirkpatrick’s four-level framework. Methods A blended programme of six face-to-face and two synchronous virtual workshops was conducted in three cities of Pakistan from November 2023 to March 2024. A total of 290 faculty members participated in NKH-guided workshops focused on ethical engagement with AI across the research process. The NKH approach was grounded in the Four-Component instructional design (4 C/ID) model and evaluated using the Kirkpatrick model across four levels: reaction, learning, behaviour, and results. Data were collected through post-workshop feedback, pre- and post-knowledge tests, and a three-month follow-up survey considering learning application and self-reported scholarly outputs. Results The study revealed high levels of participants’ satisfaction with workshop content and delivery (Level 1: mean satisfaction scores 4.4–4.95/5; Satisfaction Index = 87.2%). There was a 22% learning gain in post-test knowledge as compared to pre-test results (Level 2, p < 0.05). At Level 3, 30% of participants reported full implementation of NKH-aligned practices, 25% partial implementation, while 45% reported they had not yet applied the skills in the three-month follow-up. At Level 4, 6.2% of faculty completed full manuscript writing using AI tools, while 44% reported partial ethical integration of AI-supported practices in writing, with a significant association (p = 0.04). Conclusion The study concludes that the NKH approach supports the ethical engagement of faculty with AI tools in research, as demonstrated by enhanced satisfaction, knowledge gain, and early scholarly activities. The varied practices of faculty at level 3 and 4 suggest the need for institutional support, peer networking, and explicit guidance. The NKH approach offers a structured and pedagogically informed framework for fostering ethical engagement with AI in faculty training. Supplementary Information The online version contains supplementary material available at 10.1186/s12909-026-09405-2.

What kind of evidence is this?

Supporting AI learning science · Comparative / pre-post study reported

Stage: Adult & professional learning. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

This study employed a programme evaluation design to examine the implementation and outcomes of the NKH approach for ethical engagement with AI in faculty research education. A mixed-methods approach was used, analyzing both quantitative and qualitative data to evaluate educational outcomes during the study.

Limitations and uncertainty · source passage

The study benefited from a large multi-institutional sample and the use of both quantitative and qualitative data. Alignment with established instructional and evaluation frameworks further strengthens methodological rigour. However, several limitations should be noted.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Use a bounded job-relevant simulation or professional-development task with an expert supervisor. Require a traceable rationale and verification before any real-world action. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Assess independent performance on an unfamiliar simulation, retention after a delay, error severity and total review time. Do not equate a faster AI-assisted response with durable professional competence. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Noor-i-Kiran Naeem; Zil-e-Fatima Naeem; Asfandyar Anwer. Ethical engagement with artificial intelligence in faculty research education: evaluation of the New Knowledge Help (NKH) approach. BMC Medical Education (2026-05-21). https://doi.org/10.1186/s12909-026-09405-2

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.