Actionable companion · Assessment and evidence of learning

From evidence to district action: Generative artificial intelligence in forensic science education: Student perspectives on AI-supported crime scene decision-making

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

Evidence and context

Confirm the study population, setting and task before applying its findings to your district.

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

Abstract Students in Higher Education (HE) are increasingly engaging with Generative Artificial Intelligence (GenAI), raising concerns regarding learning, academic integrity, and equitable access to technology. In forensic science, these concerns are particularly significant, as professional practice relies on critical judgement, evidentiary reliability and ethical accountability. This study examined how undergraduate forensic science students engaged with a university-approved GenAI tool (Copilot) within crime scene simulations, with a focus on decision-making, ethical and responsible use, and trust. Copilot was embedded into a second-year subject (Major Scene Investigation) of a three-year undergraduate forensic science degree, as a structured decision-support tool. Students critically evaluated AI-generated outputs during simulated crime scene investigations and documented their use through reflective entries, including interaction records and reflections that could be presented in various formats (e.g. written, audio, video). These data were analysed thematically to explore student perceptions, patterns of engagement, and the development of critical AI literacy. Findings indicate that students did not adopt GenAI uncritically or as a substitute for professional judgement. Instead, they demonstrated a cautious selective approach, positioning GenAI as a conditional support tool. Engagement evolved over time, with initial scepticism giving way to more informed strategic use. Importantly, students differentiated between low- and high-stakes decisions, demonstrating reduced reliance on GenAI where accountability and evidentiary consequences were greater. Embedding GenAI within assessments supported the development of critical and ethical GenAI literacy, enabling students to articulate appropriate, cautious, and limited uses of the technology. While GenAI offered efficiencies in tasks such as hazard identification and documentation, its role remained bounded by the need for human oversight and justification. This study demonstrates the value of integrating GenAI into discipline specific learning environments to support critical engagement and understanding rather than passive use. It highlights the importance of explicitly teaching the ethical and professional boundaries of GenAI in forensic science decision-making to prepare students for responsible use of this technology in future practice. Keywords: Crime scene processing and management, Professional judgement, Human-AI collaboration, AI literacy, Forensic training, Higher education, Assessment and monitoring, Teaching & learning

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. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. 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. Blind-score reasoning, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. 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

Paula Tarttelin Hernández; Alicia Haines; Vincent Mousseau; Madysen Elbourne; Teneil Hanna. Generative artificial intelligence in forensic science education: Student perspectives on AI-supported crime scene decision-making. Forensic Science International: Synergy (2026-08-06). https://doi.org/10.1016/j.fsisyn.2026.100723

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.