Authentic Assessment in the Age of Artificial Intelligence: A Live Excel Interview for Agribusiness Students

1. Introduction

Microsoft Excel remains the dominant spreadsheet application requested in business job postings (Rebman et al. 2023), yet conventional assessments such as instructor-graded Excel exam files or take-home projects paired with presentations seldom verify whether students can explain and apply functions in the conversational, time-pressured setting of a job interview. While these formats strengthen communication and contextual reasoning, they still rely on submitted artifacts and rehearsed explanations that can now be substantially supported by generative AI tools, limiting instructors’ ability to verify authentic individual competence. This gap poses challenges for students entering the job market, where employers value not only technical proficiency in areas such as data cleaning, pivot reporting, and dashboard creation but also the ability to articulate their decisions and demonstrate spreadsheet knowledge in interview settings.

To address this challenge, we embedded a structured Excel interview into a 200-level Excel for Agribusiness elective, typically taken by first- and second-year students. The course is designed to build both technical and communication skills across the semester. The 10-minute interview is co-conducted by the instructor and either an undergraduate teaching assistant who previously completed the course, a graduate teaching assistant (TA), or an industry volunteer. Though conducted in person, the interview incorporates hiring-relevant elements such as situational prompts and shared-screen tasks. Questions used in the interview were developed from actual agribusiness internship experiences, student-reported interview questions from previous hiring processes, and direct conversations with industry professionals who hire graduates into spreadsheet-intensive roles. The interview includes a mid-interview feedback pause, allowing for formative correction and anxiety reduction, which is uncommon in assessment models. Anchored in performance assessment theory and mock-interview literature (Hansen et al. 2009; Bayley et al. 2024), the structured Excel interview encourages retrieval practice and integrates the STAR method (Situation, Task, Action, Result), a common interview framework that helps candidates organize concise, example-based responses, for behavioral questions. Together, these design choices reinforce students’ ability to transfer spreadsheet skills into authentic workplace contexts.

In an era of generative AI, performance-based evaluations like this interview offer a tangible way for students to demonstrate their skills in their own voice. By requiring real-time explanation and task execution, the interview addresses employer concerns about the authenticity of coursework. The objective of this article is to describe and evaluate a live, performance-based Excel interview designed to (1) verify students’ authentic technical competence in the presence of generative AI tools and (2) develop students’ ability to verbally explain spreadsheet decisions under interview-style conditions aligned with agribusiness employer expectations.

2. Literature Review

This section situates the live Excel interview within research on mock interviews, oral and performance-based assessment, authentic assessment, and employer expectations for technical communication.

2.1. Mock Interviews as Pedagogy

Prior studies demonstrate the pedagogical value of mock interviews in reinforcing content mastery, improving professional confidence, and cultivating communication skills (Hansen et al. 2009; Harchar 2005). Mock interviews are commonly structured as peer-pair exercises or faculty-led sessions and are frequently integrated into specific courses or career preparation activities (Koenigsfeld et al. 2012). These formats help reduce interview anxiety (Marcus et al. 2023), encourage reflective practice (Hudak et al. 2019), and promote student self-efficacy (Coll and Lay 2001). However, they are typically used in career-preparation or communication-focused settings and are seldom embedded into semester-long technical instruction with explicit links to industry-informed learning outcomes. By integrating an oral exam into a technical spreadsheet course, our model fills that gap while preserving rigorous content coverage. The associated learning objectives—which span technical fluency, applied reasoning, and verbal explanation—are detailed in Appendix D.

Unlike most prior models, this approach emphasizes sustained technical instruction, targets the agribusiness sector, and incorporates advanced Excel tools such as Power Query and Dynamic Arrays. The growing relevance of oral evaluation formats is underscored by HR evidence showing that 79 percent of hiring professionals rate interview performance as “very important” for entry-level roles (Mowreader 2024). While our implementation is in-person and instructor-led, recent innovations such as Concurrent Video-Based Oral Exams (ConVOEs) demonstrate that oral assessments can scale to over 600 students while retaining structured rigor (Bayley et al. 2024). Additional research affirms the value of oral exams in improving validity, communication, and integrity, particularly in online environments (Akimov and Malin 2020). In the context of AI-enabled academic dishonesty, voice-verified formats offer a renewed and credible approach to ensuring student authorship (Belkin 2023).

Communication consistently ranks among the most sought-after skills for entry-level roles in business analytics, with employers emphasizing both written and verbal abilities (Stanton and Stanton 2020). These findings highlight the need to embed communication practice directly into technical coursework. Beyond simply presenting information, Brink and Costigan (2015) argue that professional success often hinges on two-way communication, including the ability to listen, adapt, and respond thoughtfully in real time. While group discussions can foster general conversational skills, individual oral assessments offer a more direct and rigorous way to evaluate a student’s mastery of material and their readiness to engage in the kind of spontaneous, dialogue-based problem-solving that typifies workplace communication. The Excel interview described in this paper aims to build exactly that kind of fluency.

Our design emphasizes the live two-to-one interviews for richer dialogue and formative feedback. The Excel interview draws on established theories of authentic assessment, which emphasize real-world relevance, performance over recall, and context-rich evaluation (Wiggins 1990). It also incorporates elements of situated learning theory, in which knowledge is best demonstrated within the environment where it will be applied. The interview format encourages retrieval practice (Roediger and Butler 2011), reinforcing long-term retention by requiring students to generate responses under realistic conditions. By combining live technical demonstration with verbal explanation, the interview captures both procedural fluency and conceptual understanding, which are core tenets of meaningful learning. These combined strategies promote long-term mastery by bridging theoretical knowledge with authentic workplace application.

Faculty-run mock interviews improve students’ professional fluency (Hansen et al. 2009). These insights align with best practices in educational simulation and support the pedagogical rationale for our model. Stanford’s Oral Communication Tutor program recommends matching practice modality (in-person, phone, Zoom) to anticipated interview format and recording sessions for self-critique (Stanford University Oral Communication Program 2025).

While dual-interviewer formats and real-time formative feedback are established practices in clinical and professional training, such as the Multiple Mini Interview (MMI) model used in medical school admissions, they remain largely absent from undergraduate business or technical instruction. Our model draws from these adjacent fields and applies their practices to an Excel-based assessment context, an extension not previously documented in the teaching literature. By integrating these innovations into a semester-long pedagogical design, we offer a novel contribution that blends proven strategies from high-stakes assessment with practical classroom application.

2.2. Industry Expectations and Course Context

The acceleration of virtual recruiting has normalized screen-shared, think-aloud demonstrations during interviews (Belkin 2023). Employers increasingly expect candidates not only to possess technical skills but also to demonstrate them in real time, often through screen-shared problem solving and verbal walkthroughs of Excel tasks. This blend of performance and communication is especially relevant in spreadsheet-heavy roles, where the ability to explain technical processes clearly is essential.

This shift is especially relevant in agricultural sectors, where Excel is widely used for data analysis, planning, and reporting. In general, hiring managers cite Microsoft Excel more than four times as often as Google Sheets in job postings (Rebman et al. 2023). Internal feedback from agribusiness recruiters and internship supervisors further underscores the importance of advanced Excel capabilities, including PivotTables, lookup functions, scenario modeling, and dashboard creation. These are not merely “bonus” skills; they are often used as differentiators when selecting candidates for internships or entry-level roles. As Stanton and Stanton (2020) note, verbal and written communication consistently rank among the top five skills sought in analytics roles, making it essential that students not only complete Excel tasks but also articulate their reasoning and business relevance. In this context, the ability to explain Excel-based decisions is becoming just as critical as executing them.

In response to these evolving expectations, Excel for Agribusiness was designed to develop both technical fluency and communication skills. This 15-week, three-credit course meets twice weekly for 75 minutes and assumes no prior Excel experience. Instruction blends brief demonstrations with guided in-class exercises and weekly workbook projects modeled on common agribusiness workflows. Assessments include three in-class exams and a live Excel interview during the final week. While end-of-term exams and presentations highlight finished products, they still rest on submitted files. The live interview requires each student to demonstrate and explain their artifacts in real time, verifying authentic understanding.

Each mock interview is co-conducted by two evaluators, usually the instructor and either a trained teaching assistant or industry volunteer, to strengthen inter-rater reliability and expose students to varied professional perspectives. At the 5-minute mark, the interview pauses briefly for formative coaching. This feedback is explicitly framed as nonpunitive: no points are deducted for initial errors. Instead, the Responsiveness dimension of the rubric captures how effectively students incorporate feedback into subsequent explanations or task revisions. Students then resume or revise their task, implementing instructor suggestions. This midsession feedback loop helps reduce anxiety and supports in-the-moment correction, aligning with principles of formative assessment and authentic evaluation (Wiggins 1990).

The course scaffolds from foundational topics such as formulas, PivotTables, conditional formatting, and lookup functions to more advanced tools like Solver, scenario modeling, and macro automation. Students apply these skills in agriculture-specific contexts, including breakeven models, loan amortization tools, inventory sheets, and commodity dashboards. Later assignments emphasize data visualization and storytelling, with students replicating real-world trending graphs from platforms like Twitter or TikTok. Recent examples include a choropleth-style map of US soybean yields and a comparative bar chart of rare earth mineral outputs using customized Excel visuals. These projects reinforce not just technical proficiency but also audience-aware communication.

To prepare for the final live Excel interview, students engage in structured peer-practice sessions and review example videos that model both technical execution and effective verbal explanation. These activities are scaffolded throughout the semester to build applied reasoning, improvisational thinking, and verbal fluency, skills that are increasingly prioritized in hiring for data-intensive roles (Brink and Costigan 2015; Stanton and Stanton 2020).

By semester’s end, students are expected not only to execute complex spreadsheet tasks but to explain their logic and decisions under time constraints. This dual emphasis reflects a broader shift in hiring toward real-time, applied demonstration and communication. Embedding a live, mock interview into the curriculum transforms assessment from a static deliverable into an interactive professional rehearsal, one that affirms both competence and credibility.

The Excel interview is directly aligned with course outcomes that progress from basic Excel operations to high-level communication and decision-making. These outcomes represent the core competencies students must demonstrate, including applied problem-solving, technical fluency, and data storytelling tailored to agribusiness scenarios.

Table 1 summarizes these course outcomes, with a complete set of aligned learning objectives provided in Appendix D. Appendix A provides a complete instructional roadmap of weekly skills and assessments, while Appendix B contains the full interview prompt bank.^[1]

3. Interview Design and Logistics

The live interview lasts 10 minutes and is conducted by the instructor and a teaching assistant, alumnus, or industry guest. A student-facing assignment description outlining the purpose, format, expectations, and grading criteria for the live Excel interview is provided in Appendix E. It begins with a 1-minute warm-up, followed by a 3-minute conceptual Q&A, a mid-interview feedback pause, a 4-minute screen-shared Excel task, and a final 2-minute behavioral STAR (Situation, Task, Action, Result) question. These behavioral questions assess soft skills and workplace awareness by asking students to reflect on a time they identified an error, collaborated under pressure, or made a decision with limited data, scenarios that are common in internships or early-career roles.

The mid-interview pause provides students with brief formative feedback and allows students to revise part of their answer, helping reduce anxiety and encouraging reflection. Interviews may be conducted in-person or via Zoom; while recordings are not used for grading, students are encouraged to review them to reflect on their technical explanations and communication style. This optional review provides a low-stakes opportunity for self-assessment while respecting student privacy.

Interview questions are drawn from a curated 60-item bank developed using instructor observations, employer and alumni feedback, and published Excel interview compilation. Half of the questions are conceptual, focusing on why and when Excel tools are used, while the other half require execution of specific skills, such as XLOOKUP, Solver, PivotTables, and data validation. The question bank is aligned to course modules and updated each semester to maintain relevance.

Interviews were conducted across three cohorts (2022–Spring 2025). For a 30-student group, two 75-minute class periods and approximately 2.5 hours of office time were sufficient to complete all interviews. Two logistical issues warrant attention: (1) ensuring equitable access for students with limited internet connectivity, who may need campus-based options for virtual participation; and (2) minimizing interviewer bias through co-scoring and post-interview norming discussions. To support equity further, video reviews are optional and never used for grading.

To maintain the integrity of the prompt bank, instructors should rotate and refresh items each term. While it’s acceptable to reference course topics (e.g., lookup functions, dashboards), avoid sharing exact prompts in advance. Appendix B contains the full prompt set, tagged by module and outcome.

3.1. Implementation Guidelines

The following guidelines translate the interview model into practical decisions about scheduling, staffing, feedback, prompt security, preparation, accessibility, and grading.

3.1.1. Scheduling and Staffing

• Schedule all interviews within the final week by reserving two 75-minute class periods plus roughly 2.5 hours of office-hour slots for a 30-student cohort.

• Leverage former students (advanced undergraduates or alumni) as trained interviewers alongside the instructor or TA to share the load and provide peer perspectives.

• After the first two interviews, reserve a 10-minute window for both raters to compare scores and resolve discrepancies; this rapid calibration keeps scoring aligned.

3.1.2. Video and Feedback

Recording and feedback practices should support student reflection without making recordings part of the grade.

• As noted earlier, interviews may be recorded (via Zoom or in-class camera) and shared with students for optional self-review.

• A brief mid-interview feedback pause helps students course-correct in real time and reduces performance anxiety.

3.1.3. Question Bank Security

Prompt security is important because the interview depends on students demonstrating transferable skill rather than rehearsing exact answers.

• Rotate and update the 60-item bank each semester. While sharing prompts online could enable gaming, live demonstration remains essential because students must perform tasks and explain reasoning on the spot.

• Instructors may point students to the syllabus or example videos for core concepts (PivotTables, XLOOKUP, etc.) but should avoid distributing exact interview prompts in advance.

3.1.4. Preparation and Transparency

Transparent expectations help students prepare for the format without compromising the prompt bank.

• No notes or formula sheets are permitted; this mirrors industry interview conditions.

• Release the final rubric(s) two weeks before interviews so students can practice both technical tasks and verbal explanations.

• Provide sample walkthroughs or example responses (without revealing actual prompts) to clarify expectations and tie the exercise back to course content.

3.1.5. Equity and Accessibility

Accessibility planning should be incorporated before interviews begin so that the format measures Excel understanding rather than unrelated performance barriers.

• Building on earlier equity considerations, proactively arrange accommodations such as extended time, alternative input devices, and private spaces.

• Students with high-stakes anxiety, speech-related concerns, or non-native English backgrounds may find oral assessments especially challenging. To support diverse learners, we provide optional peer-practice sessions, example walkthroughs, and flexible participation formats (in-person or Zoom).

• Interviewers are also coached to avoid jargon, allow clarification pauses, and create a supportive atmosphere. Scoring focuses on clarity of explanation, not accent, for non-native speakers. These practices align with Universal Design for Learning (UDL) and ensure the interview reflects students’ true understanding, not just their comfort with public speaking.

3.1.6. Grading as Meaningful Feedback

The grading process should provide evidence of mastery while also giving students actionable feedback about their technical explanations.

• This assessment functions as an integrative course project that brings together technical execution and verbal explanation reinforced through earlier peer-practice sessions, walkthroughs, and in-class explanation of Excel workflows.

• Though grading requires effort comparable to a major report, it provides rich insight into each student’s applied competence. Co-scoring early interviews can enhance reliability.

• While students may prepare with AI tools, the live, observed format ensures that all creditable performance reflects their own skills.

3.2. Flexibility and Scalability

While the Excel interview in this course is conducted in person, the format is inherently flexible and can be adapted for remote implementation via platforms such as Zoom or Microsoft Teams. Screen-sharing preserves the real-time demonstration and verbal explanation that are central to the assessment’s goals. Instructors may also adapt the structure by extending interview length, splitting technical and verbal components, or adjusting the balance between explanation and execution to align with course constraints and learning objectives.

Rather than layering the live Excel interview on top of existing assessments, instructors may substitute it for a traditional end-of-term presentation. While project-based presentations can strengthen communication skills, they often rely on prepared narratives and allow limited opportunity for probing questions within a constrained time window. A 10-minute live interview requires a comparable amount of instructor time per student but enables real-time questioning, clarification, and demonstration of individual competence. This substitution preserves overall assessment workload while increasing confidence that performance reflects students’ own technical understanding rather than rehearsed or AI-supported artifacts.

For larger enrollments, two scalable variants are especially promising. First, alumni or industry volunteers can conduct parallel interviews using a calibrated rubric, preserving the richness of live dialogue while distributing time demands. Second, Concurrent Video-Based Oral Exams (ConVOEs) allow students to record timed responses within a secure LMS, simulating interview conditions while substantially reducing synchronous instructor time (Bayley et al. 2024).

These adaptations preserve the core pedagogical goals—authentic demonstration, verbal reasoning, and resistance to AI automation—while allowing instructors to balance rigor with feasibility across different instructional contexts.

4. Assessment Rubric

Instructors allocate 10–15 percent of the course grade to the live interview.^[2] Weighting between technical (hands-on) and verbal (explanation/professionalism) components is flexible. For example, instructors may use a 50/50 split or a heavier emphasis on one dimension, with half-points allowed for nuanced scoring. The final rubric(s) (see Appendix C) are shared in advance to guide student preparation and practice. Since most courses include few oral components, assigning 10–15 percent of the total grade recognizes students who excel at verbal articulation without disproportionately affecting overall averages.

5. Findings, Student Outcomes, and Stakeholder Reflections

Three cohorts of agribusiness majors, each with roughly 30 students (Spring 2023–Spring 2025; $N = 90$), completed the 10-minute interview. Two raters scored independently using the rubrics in Appendix C; inter-rater agreement was $\kappa = 0.82$ (hands-on) and $\kappa = 0.78$ (verbal). All students had identical prerequisite modules and practice activities. Rubric scores were exported from the LMS and analyzed descriptively; no significant semester-to-semester differences emerged (one-way ANOVA, $p > 0.10$). To assess the effectiveness and reception of the Excel-interview assignment, data were gathered under an IRB-approved protocol (#28395) over three semesters; the protocol captured voluntary student reflections, qualitative comments from employer/interviewer partners, and anonymized grade averages across cohorts. Video recording was voluntary, and 87 percent of students opted in. Most students successfully completed the hands-on Excel task within the allotted time, with minor errors more common than noncompletion. This outcome is consistent with rubric distributions showing slightly lower scores in error handling than in technical accuracy.

Student feedback was overwhelmingly positive. Although many noted that while the interview initially felt “nerve-wracking,” the mid-interview feedback pause helped them reset and respond more confidently. Several described the experience as more job-relevant than any previous academic test. One student wrote, “The interview forced me to really learn why I use these functions. I didn’t just memorize, I understood it.” Another commented, “It was practical and relevant. It directly prepared me for an actual job interview.” Others appreciated peer-practice sessions, which helped surface and address knowledge gaps. The option to review performance afterward was also impactful. One student shared, “Seeing myself on video afterward was uncomfortable but incredibly helpful. I could see every hesitation and know exactly where I needed improvement.” Many described the interview as the most valuable assignment of the course, especially because it “connected everything we learned directly to what employers actually ask.”

Industry partners echoed these sentiments. Employers who helped interview students observed strong confidence and clarity in technical explanations. A regional recruiter noted, “Students in the Excel interview were notably stronger in explaining why Excel skills matter, and they offered clear, concrete examples of how they would use Excel to solve real-world business problems.” A farm supply manager added, “These students didn’t just know Excel. They knew how to communicate it.” Others praised students’ ability to explain functions like XLOOKUP and Solver in context. One HR partner stated, “The students who participated in the mock interviews would stand out immediately during any internship recruitment.”

From an instructional standpoint, the interviews were manageable and efficient, requiring approximately 10 minutes per student, including setup and rubric completion. Quantitative analysis of rubric data revealed high levels of performance. Tables 2 and 3 display the distribution of scores across eight criteria, with most students rated as “Outstanding” or “Strong.” The highest scores were in Technical Accuracy (70 percent) and Conceptual Clarity (65 percent), while Responsiveness and Error Handling trailed slightly, highlighting areas for additional scaffolding.

Despite its strengths, the live interview model requires thoughtful planning, instructor training, time allocation, and rater calibration. Even with a shared rubric, differences in demeanor or follow-up questions may influence responses. Scaling with asynchronous oral formats introduces trade-offs, such as reduced interaction and delayed feedback, but may serve as a viable alternative when paired with peer or alumni co-raters. Because this evaluation lacked a control group and longitudinal follow-up, its findings should be considered preliminary, and future research should adopt pre-/post-test or quasi-experimental designs to isolate learning gains and assess longer-term impacts.

A key strength of this model is its resilience against AI-enabled academic dishonesty. As generative tools like ChatGPT increasingly mimic student outputs, traditional file-based assessments lose credibility. The live Excel interview restores that credibility by placing students in a timed, face-to-face setting where they must reason aloud, build formulas, and troubleshoot under observation. These time constraints surface not only technical fluency but also real-time judgment, adaptability, and communication: competencies that static submissions can easily conceal.

The interview serves as a transparent capstone: It confirms authorship; integrates technical and communicative competence; and demonstrates to students, instructors, and employers that learning outcomes have been authentically met. More broadly, it illustrates that scalable, human-centered alternatives to high-surveillance assessment are both feasible and effective when tightly aligned with course objectives and industry needs.

6. Discussion

This project confirms that a live, oral Excel assessment can be integrated effectively into an applied, technical course and still align with employer expectations. Unlike traditional mock interviews that focus mainly on communication, this format blends on-screen problem-solving, behavioral reflection, and authentic workplace tasks in a time-efficient structure, culminating in a comprehensive assessment of students’ technical fluency and professional reasoning.

Rubric data indicate strong gains in conceptual clarity and communication. Lower scores in error handling and responsiveness suggest adding earlier, low-stakes opportunities for students to practice real-time troubleshooting (see Tables 2 and 3).

Importantly, the interview also addresses rising challenges in academic integrity. As generative AI continues to erode confidence in traditional take-home assignments, instructors need scalable ways to verify student competence without turning to high-surveillance solutions. Recent scholarship recommends oral, performance-based tasks as a primary defense against AI-generated work (Mahmud 2024). The encouraging student and employer feedback reported here supports Wiggins’s concept of authentic assessment by replicating workplace conditions. It also applies situated-cognition principles by embedding problem-solving in an agribusiness context and reinforces retrieval-practice theory through timed, verbalized explanations of spreadsheet tasks. Together, these findings show that high-touch, live assessment remains viable in technical courses and may be especially valuable in fields where demonstrated skill and professional fluency shape employability.

Faculty also benefit: Co-scoring and calibration sessions build TA expertise and spark richer dialogue about logic and professional communication, areas often underemphasized in spreadsheet courses. Although the current model is implemented in a single course, its structure is transferable across other technical disciplines.

Several implementation limitations warrant consideration. Live interviews require advance scheduling, interviewer training, and periodic calibration to maintain scoring consistency. Although the mid-interview feedback pause reduces anxiety for many students, oral assessments may still pose challenges for students with high-stakes performance anxiety or limited prior interview exposure. Scalable variants such as asynchronous recordings or distributed alumni interviewers introduce trade-offs, including reduced real-time dialogue or delayed feedback. These constraints underscore the importance of aligning interview format with course goals, enrollment size, and available instructional support.

Future research should examine whether completing the interview correlates with longer-term skill retention, improved internship performance, or increased confidence in job interviews. Research could also test scalable variants (e.g., ConVOEs or parallel alumni panels) and adapt discipline-specific prompt banks in fields such as finance, marketing, or data science. Models that combine rigor, relevance, and human-centered performance will be vital as higher-education assessment evolves in the AI era.

7. Conclusion

The integration of a 10-minute live Excel interview in a spreadsheet course tackles two urgent challenges: equipping students for job interviews and safeguarding assessment integrity in the AI era. Rather than relying solely on file submissions, this performance-based assignment creates a structured opportunity for students to apply technical skills and explain their decision-making process. These are abilities that employers consistently value but rarely see assessed in coursework (Stanton and Stanton 2020).

Because it mirrors real workplace demands and was co-designed with industry input, the interview strengthens both technical mastery and communication. It helps restore trust in the learning process by ensuring that students rather than AI tools are the ones actively demonstrating competence (Belkin 2023; Cotton et al. 2024). Students report greater interview confidence, while employers note sharper clarity and poise. The accompanying prompt bank, rubric, and implementation guide give instructors a flexible template; accommodations (captioning, extended time, alternative input) and scalable variants are readily integrated under the same scoring framework.

As higher education adapts to generative AI, performance-based tasks that prioritize student voice and real-time reasoning will be essential. The Excel live interview requires live articulation under time limits, resists automation, deepens learning, and reaffirms the degree as a credible signal of workplace-ready competence. Taken together, this approach shifts spreadsheet instruction from rote file production to authentic performance, showing that even in an AI-saturated landscape, human expertise should remain central to assessment. Findings from three successive agribusiness cohorts indicate that the live Excel interview is both feasible and impactful; future studies can test how well the model generalizes to additional agribusiness courses and related technical disciplines.

Acknowledgments

The author declares no conflicts of interest and received no external financial support for this research.

AI Disclosure

Generative AI tools were used for manuscript preparation and editing, including assistance with assembling, formatting, and verifying the reference list. Generative AI tools were not used for the research design, data collection, data analysis, interpretation of results, or generation of findings. The author reviewed and is responsible for the accuracy of all citations, references, and final manuscript content.

Human Subjects Disclosure

This study was approved by the Institutional Review Board (IRB) of North Carolina State University (Protocol #28395). All participants provided informed consent prior to participation.