Luke LeFebvre*
Renée A. LeFebvre- Disability Resource Center, University of Kentucky, Lexington, KY, United States
Career readiness is central to job placement, and the development of a competitive résumé by students plays a crucial role in that process. Thus, institutions of higher education have introduced résumé AI tools to help prepare students for the workforce. AI résumé tools are software applications used to optimize résumés. This study explores students’ (N = 88) perceived usefulness of résumé AI tools and examines how these tools contributed to student learning and teaching. The results demonstrate that a majority (70.5%) of students found AI résumé tools advantageous for improving their résumés. Future research should explore how humans interpret and integrate the algorithmic feedback generated by résumé AI technology to effectively adapt their résumés.
Introduction
Career-focused experiences offered by institutions of higher education aid graduating students in obtaining and securing labor market employment (Flaherty, 2023). Approximately 1–3% of university operating budgets are allocated to student career services and employment initiatives (Alonso, 2023; Johnson et al., 2022). A number of those career service departments obtain access to virtual career platforms that offer 24*7 access and use artificial intelligence (AI) to provide real-time feedback on interviewing skills, job searches, and résumés (Mowreader, 2024). No other pre-interview marketing document is as important for students’ job searches as the résumé (Cole et al., 2007). Consequently, the résumé moderates a candidate’s ability to migrate from applicant to interview pool. For decades, institutions of higher education have recognized the importance of supporting students in developing a competitive résumé (Charney and Rayman, 1989), and at present, academic institutions are integrating AI résumé tools to assist in this endeavor (Abdelwahab et al., 2022).
AI technologies have rapidly spread across the internet with promises to enhance résumés (e.g., Kickresume, Enhancv, and handshake) and are commonly available as part of university-wide enterprise licenses, such as big interview’s (2025) ResumeAI. These generative AI tools, a subset of artificial intelligence, are engineered computer programs that generate content via the computational repetition of prevailing patterns found through exposure training with large datasets (Ott and Mack, 2025; Randazzo, 2020). The introduction of these AI technologies has started to change the way institutions of higher education approach student résumé building to enter the labor market upon graduation. AI-powered résumé tools have recently taken on a larger role in assisting learners generate, optimize, and adapt résumés (Ponce, 2024). However, it remains unclear how students perceive these emerging AI technologies in terms of using these tools to help them gain a competitive edge in résumé building.
This research report presents findings aimed at advancing and better understanding the use of AI-powered résumé tools to enhance student résumé development in educational settings. Specifically, Section 2 covers background information related to AI résumé tools purchased by institutions of higher education. In Section 3, the research method is described. Section 4 covers detailed students’ opinions and thoughts about using AI résumé tools. Finally, Section 5 concludes with a discussion of evidence-based practices to implement AI résumé tools for high-quality teaching and assessment.
AI-powered résumé tools
The résumé functions as a more complex billboard for applicants to highlight job fit, educational background, and work experience (Harcourt and Krizan, 1989; Hutchinson, 1984; Hutchinson and Brefka, 1997). An applicant’s résumé is a combination of both (a) formal features and (b) audience-based content (Randazzo, 2020). A résumé’s formal features are largely aesthetic or layout-related (i.e., format, word choice, and sectioning), whereas a résumé’s audience-based content relates to contextual prose adaptation (i.e., linguistic style match, relevant content modification for organizational correspondence, and implicit indicators). Both formal features and audience-based content influence how an applicant’s résumé will be assessed and if the applicant will transition to the interview phase in the job search.
Previous research has evidenced that résumés benefit from the integration of active words and keywords found in the job description (Diaz, 2013; Smart, 2004). Additional findings support the importance of error-free grammar and spelling in a résumé’s presentation (Charney et al., 1992; Martin-Lacroux and Lacroux, 2017). Other research has shown that organizations prefer résumés presented in a chronological sequence (Schullery et al., 2009). Based on the organization’s preference, how the résumé’s characteristics and content organization are presented becomes critical elements that distinguish top candidates from other applicants (Smith and Berg, 2020).
AI résumé tools are software applications designed to optimize résumé’ presentation (Ponce, 2024). These tools using the AI résumé technology generated nearly immediate feedback that is personalized and specific for areas of improvement to enhance the résumé by comparing job description benchmarks to the applicant’s résumé (Birt, 2024). An AI résumé tool is most effective when screening parameters are clearly defined (Boiman, 2024). Using machine learning algorithms, these tools analyze résumés and produce assessments with suggestions targeting spelling, grammar, length, keyword match, action word usage, font choice, space utilization, and margins to improve readability, formatting, and safeguard screening ability for the Applicant Tracking Systems (ATS) (Lookadoo and Moore, 2024).
The newness of the AI résumé technology provides fertile ground for classroom-based instruction (Chong, 2024). However, a review of the extant literature revealed no classroom studies examining how these AI résumé tools are perceived by students who are using the above technology to optimize their résumés. Hence, the following research questions are posed:
• RQ1: How favorable do students perceive the use of an AI résumé tool?
• RQ2: How do students perceive the usefulness of AI résumé tool feedback for enhancing their résumés?
Method
Participants
This study (N = 88) involved undergraduate students enrolled in a required, multisection business communication course at a large, east-central university in the United States. During the summer prior to the fall 2022 semester, the course instructor participated in exclusive training that introduced prototype résumé AI technology developed for the university’s previously purchased virtual career platform. Subsequently, the instructor collaborated with virtual career platform representatives to secure access and pilot a free trial of the résumé AI tool. This cutting-edge résumé AI technology was not available to other sections of the multisection course and was only used with students associated with the instructor’s course sections (n = 4) during the academic year (2022–2023).
Participants’ educational status was as follows: 1% (n = 1) sophomores, 93% (n = 82) juniors, and 6% (n = 5) seniors. All participants (100%) were advanced business majors (i.e., Accounting, Economics, Finance, Management, and Marketing). The average age of participants was 20.7 years. Gender distribution included 32% (n = 28) women and 68% (n = 60) men. Ethnic representation was 84% (n = 74) Caucasian/white, 7% (n = 6) Hispanic/Latinx, 5% (n = 4) African American/Black, and 5% (n = 4) Asian American.
Procedures
ResumeAI
Students participated in résumé development activities to build or refine their résumés during the fourth and fifth weeks of the 16-week academic semester. Prior to the résumé submission for instructor grading, students were instructed to have their résumés evaluated by the institution’s virtual career platform résumé AI-technology (see Big Interview, 2025b). The primary focus parameters for ResumeAI were set for education (as opposed to work experience). The platform offers three achievement benchmarks for feedback scores—Gold (superior), Silver (competent), and Bronze (developing). Gold was selected as the scoring benchmark. A mandatory feature was enabled that required the job description to be uploaded to ResumeAI prior to the scanning of résumé using AI, and each student could have their résumé scanned for a maximum of five times each day for 1 week.
The AI résumé elements were customized across four categories: readability, credibility, format, and ATS fit. Readability assessed the résumés’ first impression aesthetics, content information inclusion, summary statement, spelling, grammar, and pronoun usage. Credibility evaluated experience information (chronological ordering and description statements), education details, skill focus (relevance and match), and competency match to job description. Format gauged font size and choice, margins, line spacing, bullet point format, date format, and length of résumé. ATS fit estimated keywords, skills, competency, job title, education, experience level, and location to match with the job description.
Participant’s AI résumé technology use and overall ranks earned from the AI are shared in this study for situating user perceptions of the technology. The rankings could not be associated with participant’s individual feedback due to confidentiality purposes, and no identifiable information was collected from the survey responses by a separate institutional entity. Participants submitted résumés (N = 244) to the AI résumé tool, with a majority of participants submitting their résumés multiple times (M = 2.77) in an attempt to improve their résumé ranking. The majority of participants earned competent or superior résumé rankings (77%) from the AI: 31% (n = 27) Gold (superior), 46% (n = 41) Silver (competent), and 23% (n = 20) Bronze (developing).
Survey mechanics
As part of a midsemester feedback report (week 9 of the semester)—collected by a separate institutional entity without the instructor being present—students agreed to respond to an open-ended question regarding their use of résumé AI. The collected student responses occurred during class time, via a Doodle response pool where students responded from their cellphones. No identifying information was collected from participants. The Institutional Review Board (IRB) chairperson or designee determined that this study did not require IRB review because it fulfills the Coded Private Information or Specimen Use in Research requirements (U.S. Department of Health and Human Services, 2008). Students were asked to discuss (a) what has helped them succeed in the course and (b) what could help them succeed further. They were encouraged to think about the course holistically, including the instructor, course design, materials, lessons, and assignments. The instructor requested that the institutional entity representatives develop a question to gauge how students felt about the use of the prototype AI résumé technology. Specifically, the representatives asked students the following questions about AI résumé tool usage: “Did you find using the AI résumé technology a useful way to receive feedback on your résumé? Why or why not?”
Data analysis
AI résumé favorability
For this study, the two-part question posed to participants was segmented for analysis. The initial question—“Did you find using the AI résumé technology a useful way to receive feedback on your résumé?”—was coded with a priori themes derived from previous research on types of feedback (i.e., LeFebvre et al., 2010; Mory, 2003; Waldersee and Luthans, 1994). Each participant’s response to this question was unitized (N = 88) in the form of the word (i.e., Yes or No) and coded as either positive (Yes) or negative (No). For example, one student wrote, “Yes because it provides plenty of examples that can help me effectively build my résumé and provides useful feedback.” The affirmative response “Yes” was coded as a positive perspective about the use of AI résumé technology. Two naïve coders were provided with the category scheme and categorical definitions (see Table 1) for both positive and negative responses before coding 20% of the sample independently and produced a Krippendorff’s alpha of 1.00, which is in perfect agreement. One coder then returned to the dataset to code the remaining data using the devised codebook.
Table 1. Student perceptions of AI résumé technology’s usefulness.
AI résumé usefulness
Participants then provided qualitative commentary to the subsequent question—“Why or why not?”—about the use of the AI résumé technology. We (two authors) used an open coding process to employ emergent thematic analysis (Tracy, 2013) to examine the responses to the open-ended question. Using an open coding process, we identified potential patterns across participants’ comments to identify and develop similar conceptual thoughts and patterns (Patton, 2015). We independently reviewed open-ended responses and generated a list of reasons. We met and synthesized participant observations into a list of primary reasons. After organizing these conceptual thoughts, we discussed and integrated the codes into themes. Finally, we settled on six themes. A random sample of 10% of the responses was selected for coding. Each author independently coded the responses. Interrater reliability was calculated using Krippendorff’s alpha, which obtained 0.98 agreement. We reviewed the remaining disagreements after coding to complete the analysis. After the coding process, we then structured the themes by developing explicit meanings and explanations, drawing illustrative examples based on particular themes for each coding scheme. We preserved the integrity of the original wording of our participants. Table 1 displays the themes and frequencies.
Results
AI résumé favorability (RQ1)
To address RQ1, participants were asked if using the AI résumé technology was a useful way to receive feedback on their résumés. The majority of participants reported that they perceived using the AI résumé tool positively (70.5%) compared to those students who viewed its use negatively (29.5%).
AI résumé feedback usefulness (RQ2)
To address RQ2, participants provided information about why they perceived the AI résumé tool as either useful or not useful for receiving feedback about their résumés. A majority of participants (n = 62) noted a positive perception, recognizing the benefits of using the AI résumé technology to receive feedback about their résumés. These perceptions included affirmative responses indicating that students found the tool helpful for résumé development. Three positive themes emerged from the student’s commentary. Students identified corrective feedback (59.7%) as the most positively perceived feature of the résumé AI. For example, one student shared, “…[AI] giving feedback very quickly and if you have something else to do, you can go ahead to do so, as soon as the feedback is given, you can change immediately.”
The second most discerned positive résumé AI feature was perceived helpfulness (25.8%). Students felt as if AI résumé technology was helpful in résumé development. For example, a student shared, “I did find it useful because, with the grading that it gives you, it tells you specifically what to change in your résumé. Also, I found the class itself very helpful in my résumé building.” Another student described using AI résumé tools as, “…[AI] provides plenty of examples that can help me effectively build my résumé and provides useful feedback.”
The final positive aspect was specificity-particularness (14.5%). For example, a student felt that “… it does not settle for much less than perfection.” Another remarked that “…it was great being able to use such a software to make sure my résumé was top notch.”
A minority of participants (n = 26) responded with a negative perception related to using the AI résumé technology to receive feedback on their résumé. These perceptions consisted of rejection responses, in which students found AI résumé technology to be unhelpful for résumé development. Three negative themes emerged from the student commentary. Students identified inaccurate criticism (19.3%) as the most negatively perceived aspect of the résumé AI. Students suggested that the AI would provide flawed suggestions for improving their résumés. For example, one student expressed that, “… it was difficult to know exactly what it was marking off points for and if you changed one thing to try and help gain points, you could potentially lose points in another category.”
The second most frequently articulated negative aspect was how the AI résumé tool was not helpful (5.7%). Students felt as if AI was not as helpful as other feedback sources (i.e., instructor). For example, a student shared, “Not as much as feedback from the instructor, because it’s online.”
The third negative issue was that the AI résumé was confusing to use (4.5%). A student described using an AI résumé tool “… to be confusing. It told you what was wrong but not how to make it better.”
Discussion
Institutional investments in AI résumé technology will no doubt continue as support systems for students, especially as AI becomes more pervasive (Big Interview, 2025a; Mowreader, 2024). Students will find the algorithmic feedback conveniently accessible and helpful, as the majority of students associated with this study reported. The customized suggestions, helpfulness in ranking the résumé, and particularness of the feedback are attractive for learners, as reported in this study, especially if such feedback results in higher quality résumés for student grades (and job interviews). These positive outcomes experienced by the majority of participants in this study are promising for the use of this emerging technology. However, the AI résumé technology left nearly a third of the participants negatively frustrated with the prototype technology due to inaccurate suggestions, a lack of helpfulness in the feedback, or confusion about their résumé.
Implications for teaching and learning
From an instructional vantage point, integrating AI résumé technology allowed students to receive “unbiased” feedback from a source that is attributed as neutral prior to instructor’s grading. Another source for résumé feedback is optimal if it is beneficial for the students’ résumé development. Moreover, a majority of students in this study perceived the AI résumé tool positively. However, we would urge caution, particularly if instructors (or institutions) are considering completely offloading résumé feedback to an AI algorithm, in particular, for students who have not received any prior résumé training before enrolling in the course used for this study. The concerns are two-fold: (1) students may not yet understand which feedback from the AI is valid and (2) how AI functions when providing feedback about the résumé. For example, a student shared in their survey response that the AI résumé technology “…gives me someone else’s point of view who is not biased and does not know me.” The statement is both disconcerting and inaccurate because AI is neither a sense-making technology nor is it intelligent (Ott and Mack, 2025). As Han (2022) asserted, “Artificial intelligence does not reason, it computes. In place of argument, there are algorithms” (p. 36). AI résumé technology is a tool to be used to enhance basic résumé development skills, but students should be reminded to be cognitively aware (or wise consumers of its feedback) and understand the limitations presented in blindly trusting algorithmic feedback.
The AI résumé used in this study was a single assignment for students, where they uploaded their résumés multiple times for AI scanning/ranking. When and how often students engage with technology throughout the semester is an important consideration for instructors, regardless of student’s perceptions of positivity. Previous research has demonstrated that the repeated use of AI teaches students technological dependence (Gerlich, 2025) to avoid affective reflexivity (Ott and Mack, 2025) and mindlessly follow algorithmic patterns based on predictive replication (Coeckelbergh, 2025). Therefore, instructors should find an appropriate balance between AI and human cross-checking for the résumé. If the goal of a higher education institutional is to help prepare future leaders and problem solvers (i.e., students) to enter the labor force, emphasizing the importance of human critical thinking in educational settings in helping students further prepare to be the arbitrators of AI tools (not the inverse).
Coupling résumé AI technologies with human-focused educational strategies that proceed and then promote AI usage and critical thinking skills is imperative (Gerlich, 2025). Educators should consider the stages of résumé development in the course. For instance, where would the résumé AI technology be best situated? Perhaps résumé AI tehcnology is best used as a final quality check, rather than simply offloading the instructional endeavor of résumé writing to an AI. Additional considerations should include the degree of human feedback a student’s résumé has received before scanning with an AI résumé tool. When and how many human-generated feedback (i.e., instructor and peers) encounters have occurred with the résumé prior to AI scanning? Such human feedback integration helps to balance the formal and audience-centered approaches to résumé creation as well as the human and algorithmic feedback provided to the résumé’s creator.
Limitations and future directions
This study explored students’ perceptions of the usefulness of AI résumé tool within a multisection, required business communication course. Student experiences were isolated to explore their perceptions of the AI technology. However, the sample only included course sections taught by one instructor with the use of a single prototype AI résumé tool. By increasing the sample size and scope, future studies should explore nuances in particular multisection courses with different instructors to determine if AI résumé technology is perceived as useful as it was to students in this sample.
Future research should seek to triangulate the association between student’s self-reported perspectives and the quality of their résumé report generated by AI résumé tools. However, students who receive higher evaluations from the résumé AI technology rate the use of the AI more positively but may not be creating the most competent résumés by human standards. Other future studies should trace the evolution of the résumé’s development from student-scanned uploads to isolate the résumé’s progression and explore if the student résumé demonstrates improvement from algorithmic feedback generated by the résumé AI.
Conclusion
AI résumé tools offer academic institutions an additional avenue to provide efficient and accessible support to students preparing application materials—particularly the résumé. The institutional goal is to help increase student competitiveness in the labor market and increase the probability of employment. The results reported in this study demonstrate that students find engaging with these technologies to be advantageous for fine-tuning their résumés. However, as résumé AI technology becomes more widespread, significant instructional training will need to be dedicated to help learners distinguish the value communicated by these algorithms.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
The studies involving humans were approved by Desiree PennAdministrative Services Assistant SrOffice of Research Integrity. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.
Author contributions
LL: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. RL: Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research and/or publication of this article.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The authors declare that no Gen AI was used in the creation of this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
Abdelwahab, H. R., Rauf, A., and Chen, D. (2022). Business students’ perceptions of Dutch higher educational institutions in preparing them for artificial intelligence work environments. Ind. High. Educ. 37, 22–34. doi: 10.1177/09504222221087614
Alonso, J. (2023). Career centers get a makeover. Inside Higher Education. Available online at: https://www.insidehighered.com/news/students/careers/2023/06/07/bigger-budgets-and-higher-profile-college-career-centers (Accessed February 8, 2025).
Big Interview. (2025a). Big interview for higher education. Skillful Communications, Inc. Available online at: https://www.biginterview.com/higher-education (Accessed February 8, 2025).
Big Interview. (2025b). Resume AI: re-write your resume in minutes. Skillful Communications, Inc. Available online at: https://www.biginterview.com/resume-ai (Accessed February 8, 2025).
Birt, J. (2024). How to optimize your resume for AI scanners (with tips). Indeed: Career Guide. Available online at: https://www.indeed.com/career-advice/resumes-cover-letters/resume-ai (Accessed February 8, 2025).
Boiman, K. (2024). What do employers think about AI resume builders? LinkedIn. Available online at: https://www.linkedin.com/pulse/what-do-employers-think-ai-resume-builders-keren-boiman-mba-umexc (Accessed February 8, 2025).
Charney, D. H., and Rayman, J. R. (1989). The role of writing quality in effective student résumés. J. Bus. Tech. Commun. 3, 36–53. doi: 10.1177/105065198900300102
Charney, D. H., Rayman, J., and Ferreira-Buckley, L. (1992). How writing quality influences readers’ judgements of résumés in business and engineering. J. Bus. Tech. Commun. 6, 38–74. doi: 10.1177/1050651992006001002
Chong, N. S. T. (2024). The AI resume revolution: A double-edged sword for recruiters. United Nations University. Available online at: https://c3.unu.edu/blog/the-ai-resume-revolution-a-double-edged-sword-for-recruiters (Accessed February 8, 2025).
Coeckelbergh, M. (2025). LLMs, truth, and democracy: an overview of risks. Sci. Eng. Ethics 31:4. doi: 10.1007/s11948-025-00529-0
Cole, M. S., Rubin, R. S., Feild, H. S., and Giles, W. F. (2007). Recruiters’ perceptions and use of applicant résumé information: screening the recent graduate. Appl. Psychol. 56, 319–343. doi: 10.1111/j.1464-0597.2007.00288.x
Diaz, C. S. (2013). Updating best practices: applying on-screen reading strategies to résumé writing. Bus. Commun. Q. 76, 427–445. doi: 10.1177/1080569913501860
Flaherty, C. (2023). Students sound off on career centers. Inside Higher Education. Available online at: https://www.insidehighered.com/news/student-success/life-after-college/2023/11/30/survey-what-college-students-want-career (Accessed February 8, 2025).
Gerlich, M. (2025). AI tools in society: impacts on cognitive offloading and the future of critical thinking. Societies 15:6. doi: 10.3390/soc15010006
Harcourt, J., and Krizan, A. C. (1989). A comparison of résumé content preferences of fortune 500 personnel administrators and business communication instructors. J. Bus. Commun. 26, 177–190. doi: 10.1177/002194368902600206
Hutchinson, K. L. (1984). Personnel administrators’ preferences for résumé content: a survey and review of empirically based conclusions. J. Bus. Commun. 21, 5–14. doi: 10.1177/002194368402100401
Hutchinson, K. L., and Brefka, D. S. (1997). Personnel administrators’ preferences for résumé content: ten years after. Bus. Commun. Q. 60, 67–75. doi: 10.1177/108056999706000206
Johnson, C., Gitay, R., Abdel-Salam, G., BenSaid, A., Ismail, R., Naji Al-Tamemi, R. A., et al. (2022). Student support in higher education: campus service utilization, impact, and challenges. Heliyon 8:e12559. doi: 10.1016/j.heliyon.2022.e12559
LeFebvre, L., LeFebvre, L. E., Vultee, F., Pensoneau-Conway, S. L., and Swenson-Lepper, T. R. (2010) The relationship between self-generated corrective feedback and speaking performance. Top three paper presented to the basic course division at National Communication Association, San Francisco, California
Lookadoo, K., and Moore, S. (2024). Is your résumé/textbook up-to-date? An audit of AI ATS résumé instruction. Bus. Prof. Commun. Q. doi: 10.1177/23294906231223101
Martin-Lacroux, C., and Lacroux, A. (2017). Do employers forgive applicants’ bad spelling in résumés? Bus. Prof. Commun. Q. 80, 321–335. doi: 10.1177/2329490616671310
Mory, E. H. (2003). “Feedback research revisited” in Handbook of research on educational communication and technology. eds. D. H. Jonassen and P. Harris (Mahwah, NJ: Lawrence Erlbaum), 745–783.
Mowreader, A. (2024). Personalizing and increasing access in career services. Inside Higher Education. Available online at: https://www.insidehighered.com/news/student-success/life-after-college/2024/12/03/four-models-career-innovation-higher-ed# (Accessed February 8, 2025).
Ott, B. L., and Mack, R. L. (2025). Critical media studies: an introduction for the digital age. 4th Edn. Hoboken, NJ: Wiley-Blackwell.
Patton, M. Q. (2015). Qualitative research & evaluation methods: integrating theory and practice. 4th Edn. Thousand Oaks, CA: Sage.
Ponce, T. M. (2024). Ai resume writing: how prompt confidence shapes output and AI literacies. Bus. Prof. Commun. Q. doi: 10.1177/23294906241273317
Randazzo, C. (2020). A framework for résumé decisions: comparing applicants’ and employers’ reasons. Bus. Prof. Commun. Q. 83, 409–433. doi: 10.1177/2329490620963133
Schullery, N. M., Ickes, L., and Schullery, S. E. (2009). Employer preferences for résumés and cover letters. Bus. Commun. Q. 72, 163–176. doi: 10.1177/1080569909334015
Smart, K. L. (2004). Articulating skills in the job search: proving by example. Bus. Commun. Q. 76, 198–205. doi: 10.1177/1080569904265300
Smith, A. B., and Berg, M. D. (2020). Business professionals’ rankings of applicants’ résumés: updated considerations for résumé instruction. J. Bus. Strateg. 37, 55–80. doi: 10.54155/jbs.37.1.55-80
U.S. Department of Health and Human Services. (2008). Coded private Information or specimens use in research, guidance. Office of Human Research Protections. Available online at: https://www.hhs.gov/ohrp/regulations-and-policy/guidance/research-involving-coded-private-information/index.html (Accessed February 8, 2025).
Keywords: résumés, artificial intelligence, business communication pedagogy, communication education, résumé screening
Citation: LeFebvre L and LeFebvre RA (2025) RESEARCH-AI résumés: learning to improve self-presentation for the labor market. Front. Educ. 10:1576196. doi: 10.3389/feduc.2025.1576196
Edited by:
Amy Wanyu Ou, University of Gothenburg, SwedenReviewed by:
Priya Devi Gunesh, University of Mauritius, MauritiusMichele Jackson, Michigan State University, United States
Copyright © 2025 LeFebvre and LeFebvre. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Luke LeFebvre, bHVrZS5sZWZlYnZyZUB1a3kuZWR1