EMERGING DIALOGUES IN ASSESSMENT

September 18, 2025

• Amy J. Heston, Ph.D., Leader, Center of Academic and Professional Enrichment Academic Excellence Pillar, Professor of Inorganic Chemistry, Walsh University

• Neil G. Walsh, Ph.D., Chair, Division of Mathematics and Sciences, Associate Professor of Chemistry, Walsh University

• Maddox A. Kelly, Student researcher, Walsh University

• Justin J. Nienaber, Student researcher, Walsh University

• Daniel E. Palanceanu, Student researcher, Walsh University

• Maria R. Purcell, Student researcher, Walsh University

• Judy Salemdawod, Student researcher, Walsh University

• Tatiana C. Tolson, Student researcher, Walsh University

• Lexi P. Twaddle, Student researcher, Walsh University

With a shared vision to serve the common good, a research team utilized AI tools to investigate how AI technology enhanced scientific research. An innovative model included a stepwise approach that resulted in research findings and advice for future students, particularly those with majors in biology, chemistry, clinical laboratory science, or research projects in forensic biology and chemistry assessment. The overall assessment revealed that this model served the common good through alignment with the university mission, effective collaboration, student leadership, AI innovation, and its application to high impact practices. 

Introduction

With a shared vision to honor the founders of Walsh University, the Brothers of Christian Instruction, and to serve the common good through service to other students on campus and in alignment with the institutional mission, a research team was created to explore how AI technology could enhance science research. The team consisted of chemistry faculty and undergraduate students (two sophomores, three juniors, two seniors). The primary goal of this research project was to use AI tools in the investigation of how AI technology impacted science-related fields such as biology, chemistry, clinical laboratory science, forensic biology, and chemistry assessment. An innovative model, utilizing a stepwise approach, was created that included shared examples of research findings and advice for future students in science. More specifically, the topics concentrated on healthcare, green chemistry, clinical laboratory processes, forensic biology, and course design including chemistry assessment. Students began their scholarly pursuits independently and then worked collaboratively to share perspectives with team members. In addition, this work aligned with three High Impact Practices: undergraduate research, common intellectual experience, and collaborative projects (Kuh, 2008). Therefore, this work provided students with an enriched learning experience.  

Creating Our Own Research Model

The student researchers selected examples to share with Walsh students and beyond. The research model included a student-centered approach with the following steps: 1) The students were asked to summarize their findings in their own voices 2) They reflected on their learning journeys and 3) Students shared perspectives by writing some advice to help future students. Considering student voice and choice, the students were given flexibility in the degree of sharing within their individual projects and perspectives (reflections).

Research Examples and Student Perspectives

There were seven projects having student-driven research pathways. Project 1, “Dual Powers of Innovation: AI Technology and Quality Matters,” focused on how AI supported learner assessment in Principles of Chemistry I Laboratory (CHEM 101L) and course design in alignment with Quality Matters (QM) standards. (Quality Matters, 2023) Specifically, this work explored various AI tools to create module-level learning outcomes (MLOs), provide effective instructional content for CHEM 101L, employ AI prompt design strategies (prompt engineering), evaluate AI output through critical analysis, and authenticate instructional materials. The student researcher analyzed MLOs for measurability, aligned instructional videos to MLOs, and embedded the most desired content into the course shell in alignment with QM standards (Quality Matters, 2023). One additional topic for this project included creating two assessment plans to evaluate student achievement.

Project 2 described forensic biology through a student project “AI and Forensic Biology: Innovations and Applications”. The researchers found that forensic biology skills played an important role in analyzing tissue and cellular evidence as well as     determining the time of death and cause of death. Additionally, they discovered relationships with AI for identifying and evaluating shoe prints, applying AI to facial and clothing recognition, and analyzing ballistic fingerprinting. Diving deeper, this researcher found that AI was used to make a 3D reconstruction of a crime scene and analyzed microbiome data found in trace evidence thereby providing information on the individual, type of trace, and time of deposition. Other topics of interest were the applications of AI in areas such as pattern analysis and wound interpretation.

In Project 3, “AI for Use in Advancing Green Chemistry for Medicine,” a student researcher explored high energy consumption and pollution. AI applications in drug development included accelerated chemical modeling, drug simulations, and novel chemical structure generation. AI demonstrated the capacity to reduce drug production carbon footprints by identifying sustainable materials, less toxic solvents, and optimal reaction conditions. Other important issues included supply chain optimization, ethical and logistical concerns, potential biases in AI learning, and the environmental impact of AI development itself.

Project 4 described more AI applications through, “Revolutionizing Internal Medicine with AI.” The student researcher described how AI made a positive impact on internal medicine and early disease detection. For genomics, AI algorithms optimized genome editing methods like CRISPR-Cas9 and advanced genetic data analysis through artificial neural networks. This technology was crucial in unraveling genetic patterns, improved genome sequencing processes, and identified genetic variants linked to diseases. Other topics included data quality concerns, privacy issues, transparency in AI processes, and equitable and reliable healthcare concerns.

In Project 5, "Transforming Healthcare Through AI Intervention", one student researcher investigated AI innovation in the clinical setting. She described how AI predictive analytics identified high-risk patients, enabling timely interventions that drastically improved outcomes. Further, the researcher described how picture algorithms can learn from countless medical images, diagnosed conditions like lung cancer, and provided professionals with accurate information. Additional content included the impact of AI on personalizing treatment plans, tailoring therapies, improving decision-making, ensuring timely feedback, and increasing safety.

Regarding Project 6, “Revolutionizing Clinical Labs with Artificial Intelligence”, the student researcher discovered that AI increased efficiency in clinical laboratories processes by turning routine, labor-intensive processes into seamless, automated processes. Moreover, she found that AI enabled professionals and fellow researchers to be successful in their careers by increasing their accuracy in diagnostic work and creating predictive models for diseases. Other topics included personalizing patient care, analyzing genetic markers, and creating personal interventions.

Project 7, “AI's Impact on Cardiovascular Disease,” focused on how AI is revolutionizing cardiovascular medicine. The researcher reported how AI acted with human-like intelligence to analyze data, aided in medical decision-making, and showed significant improvements in the detection and treatment of heart conditions such as atrial fibrillation and coronary artery disease. Furthermore, machine learning was used in the analysis of electronic health records and medical imaging, predictive modeling for patient risks, and the development of personalized treatment plans She also explored AI implementation in clinical settings, improving patient outcomes, ethical considerations, and data privacy concerns.

Alignment with High Impact Practices    

Collectively, this work aligned with three high impact practices as described by Kuh: undergraduate research, common intellectual experience, and collaborative projects. (Kuh, 2008). First, the project enhanced undergraduate research through the use of AI tools and its technological applications. Next, each researcher was truly involved in a common intellectual experience as the team strived toward academic innovation through AI technology. The guiding light of this experience was service to others. Lastly, this collaborative project was unique because the team included students at different academic levels. This increased diversity within the team, particularly allowing students to consider ideas that were different from their own.

Overall Assessment of Project

The assessment of this project included several criteria: alignment with mission, effective collaboration, student leadership, AI innovation, and model design. Assessment of this overall experience revealed some strengths and challenges. Strengths included multi-learner collaboration, student leadership while serving as AI pioneers on campus, project alignment with the Walsh mission by serving others and contributing to innovation and creating this research model that included advice for future scientists. One innovation surprise that the team discovered was that the students’ perspectives benefited not only scientists, but all researchers from any discipline. Challenges included incorporating all projects together and designing the model. After the team shared opinions related to the overall learning experience and noted the benefits to academic research, they concluded that the model can serve as a valuable tool to help students with any range of experience with AI technology.

These assessment trends, including AI’s positive impact to the overall learning experience and benefits to academic research, guided the next steps in the students’ scholarly pursuits. Due to the successes from the multi-learner collaboration, the research group decided to create conference proposals for an undergraduate research conference. All proposals were accepted for presentation (two oral presentations, two poster presentations). Their scholarly work was shared with students, faculty, and administrators at the 2025 National Conference on Undergraduate Research. This conference was a great opportunity to showcase their efforts because their perspectives benefited not only scientists, but all student researchers from all fields. Consequently, these presentations supported student success in the use and application of AI through peer leadership in undergraduate research strategies and technological innovation in academics.

Details Regarding the AI Tools Utilized in This Project    

Considering all the projects above, a wide range of AI tools were utilized. The researchers used free versions of ChatGPT, Claude, Gemini, Copilot, Perplexity, ResearchRabbit, Connected Papers, and NotebookLM. These tools were beneficial to advance undergraduate research in several scientific disciplines. Overall, assessment of the use and application of these technologies revealed that the most effective chatbot was Gemini and the most efficient literature research tool was ResearchRabbit. Gemini produced the most accurate data and ResearchRabbit illustrated the most direct connections and relationships of one peer-reviewed artifact to another. As a result, students utilized Gemini and ResearchRabbit the most throughout this work. 

Recommendations

The following statements are researchers’ recommendations based on this work:

1. With AI use, students should be aware of AI hallucinations, such as providing inactive links, which can unfortunately result in no valid content.
2. Try using multiple tools together, like ResearchRabbit and NotebookLM, to streamline the research and writing processes.
3. AIs, like Research Rabbit and Claude, can greatly aid in the research process, but it is extremely important to bring your own ideas and creativity to make the best projects possible.
4. AI is a powerful tool that can provide valuable information, but its accuracy and the way it acquires information should be used cautiously and in moderation.
5. My best advice regarding AI use is to get results from multiple chatbots because then you will get a greater variety of answers!
6.  The best advice I can give to future students is to use AI responsibly as a tool that enhances human capabilities, always balancing innovation with ethical considerations like data privacy and fairness to ensure that AI serves the greater good.
7. The best advice I can give  is to use AI as a tool or guide, but always keep ethics in mind and make sure you're learning, not just taking shortcuts.

Conclusion

Students and faculty were successful in using AI tools to investigate how AI technology impacts science research. As a result, the team created an innovative model, focused on a stepwise approach to AI application, that resulted in student success, career readiness, and advice for future students, particularly those with science-related majors. This work made a positive impact on Walsh University.

Moreover, the project’s alignment with three high-impact practices proved beneficial for both students and faculty alike. Consequently, with a shared vision to honor the Brothers of Christian Instruction, AI innovation at Walsh University served the common good by enhancing academic research, increasing student achievement in collaborative research, and developing student leaders in the implementation of new technology.