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2018
Volume 77, Issue 1
  • ISSN: 0010-096X
  • E-ISSN: 1939-9006

Abstract

In response to disruptions introduced to the job market by AI resume screeners, this article introduces a novel theoretical framework for the life cycle of artificial intelligence systems to help unblackbox resume screening AI systems. It then applies the AI life cycle framework to a digital case study of RChilli’s job-resume matching algorithm. The article introduces an eleven-step computational job-resume matching assignment that writing instructors can use in their classrooms to explore the pedagogical implications offered by the AI life cycle framework. The assignment helps students simulate important phases in AI production and development while highlighting biases and ethical concerns in AI screening of resumes. By exploring job-resume analytics, this study helps to teach critical AI and data literacy, make job-resume matching algorithms more explainable, and transform how professional writing can be taught in the age of automated hiring.

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/content/journals/10.58680/ccc2025771112
2025-09-01
2026-06-06
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References

  1. Adadi Amina, and Berrada Mohammed. “Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI).” IEEE Access, vol. 6 2018, pp 52138–60. IEEE Xplore, https://doi.org/10.1109/ACCESS.2018.2870052.
    [Google Scholar]
  2. Angwin Julia, et al. “Machine Bias.” ProPublica, 23May 2016, https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing. Accessed 19 Feb. 2024.
    [Google Scholar]
  3. Arnulf Jan Ketil, et al. “Impression Making by résumé Layout: Its Impact on the Probability of Being Shortlisted.” European Journal of Work and Organizational Psychology, vol. 19, no. 2 2010, pp 221–30.
    [Google Scholar]
  4. Benichou L. “The Role of Using Chat-GPT AI in Writing Medical Scientific Articles.” Journal of Stomatology, Oral and Maxillofacial Surgery, vol. 124, no. 5 2023, pp 1–3. https://doi.org/10.1016/j.jormas.2023.101456.
    [Google Scholar]
  5. Benjamin Ruha. Race After Technology: Abolitionist Tools for the New Jim Code. John Polity Press 2019.
    [Google Scholar]
  6. Birt Jamie. “How to Optimize Your résumé for AI Scanners (with Tips).” Indeed, 30Dec 2022, https://www.indeed.com/career-advice/résumés-cover-letters/résumé-ai. Accessed 8 July 2023.
    [Google Scholar]
  7. Broussard Meredith. More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech. MIT P 2023.
    [Google Scholar]
  8. Buolamwini Joy. Unmasking AI: My Mission to Protect What Is Human in a World of Machines. Penguin Random House 2023.
    [Google Scholar]
  9. Cardon Peter, et al. “The Challenges and Opportunities of AI-Assisted Writing: Developing AI Literacy for the AI Age.” Business and Professional Communication Quarterly, vol. 86, no. 3 2023, pp 257–95. https://doi.org/10.1177/23294906231176517.
    [Google Scholar]
  10. Casey Kevin. “How to Get Your résumé Past Artificial Intelligence (AI) Screening Tools: 5 Tips. The Enterprisers Project, 14Mar 2021, https://enterprisersproject.com/article/2021/3/artificial-intelligence-ai-screening-tools-how-build-résumé-5-tips. Accessed 8 July 2023.
    [Google Scholar]
  11. Chen Zhisheng. “Ethics and Discrimination in Artificial Intelligence-Enabled Recruitment Practices.” Humanities & Social Sciences Communications, vol. 10 2023, pp 1–12. https://doi.org/10.1057/s41599-023-02079-x.
    [Google Scholar]
  12. Crawford Kate. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale UP 2021.
    [Google Scholar]
  13. Crenshaw Kimberle. “Mapping the Margins: Intersectionality, Identity Politics, and Violence Against Women of Color.” Stanford Law Review, vol. 43, no. 6 1991, pp 1241–99.
    [Google Scholar]
  14. Data Science Process Alliance. “What Is CRISP DM?” Data Science PM, 9Dec 2024, https://www.datascience-pm.com/crisp-dm-2/.
    [Google Scholar]
  15. Diakopoulos Nicholas. “Accountability in Algorithmic Decision Making.” Communications of the ACM, vol. 59, no. 2 2016, pp 56–62, https://doi.org/10.1145/2844110.
    [Google Scholar]
  16. D’Ignazio Catherine, and Klein Lauren F.. Data Feminism. MIT P 2020.
    [Google Scholar]
  17. Dobrin Sidney I.AI and Writing. Broadview Press 2023.
    [Google Scholar]
  18. Eubanks Virginia. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press 2018.
    [Google Scholar]
  19. Fyfe Paul. “How to Cheat on Your Final Paper: Assigning AI for Student Writing.” AI & Society, vol. 38, no. 4 2023, pp 1395–405. https://doi.org/10.1007/s00146-022-01397-z.
    [Google Scholar]
  20. Gallagher John R. “Writing for Algorithmic Audiences.” Computers and Composition, vol. 45 2017, pp 25–35. Science Direct, https://doi.org/10.1016/j.compcom.2017.06.002.
    [Google Scholar]
  21. Gallagher John R.. “The Ethics of Writing for Algorithmic Audiences.” Computers and Composition, vol. 57, Sept 2020, pp 1–9Science Direct, https://doi.org/10.1016/j.compcom.2020.102583.
    [Google Scholar]
  22. Gerson Sharon J., and Gerson Steven M.. Technical Communication: Process and Product. 9th ed. Pearson 2013.
    [Google Scholar]
  23. Guffey Mary Ellen, and Loewy Dana. Business Communication: Process & Product. Cengage Learning 2017.
    [Google Scholar]
  24. Hamilton Isobel Asher. “Amazon Built an AI Tool to Hire People But Had to Shut It Down Because It Was Discriminating against Women.” Business Insider, 10Oct 2018, https://www.busines-sinsider.com/amazon-built-ai-to-hire-people-discriminated-against-wom-en-2018-10. Accessed 19 Feb. 2024.
    [Google Scholar]
  25. Hebert Marsha. “How to Make an ATS-Friendly résumé - Tips for ATS 2024.” Toprésumé, 20Aug 2023, https://www.toprésumé.com/career-advice/what-is-an-ats-résumé.
    [Google Scholar]
  26. Ishizaki Kojiro. AI Model Lifecycle Management: Overview. IBM 2020.
    [Google Scholar]
  27. Johnson Khari. “One Startup’s Plan to Help Africa Lure Back Its AI Talent.” Wired.com, 17Feb 2023, https://www.wired.com/story/one-startups-plan-to-help-africa-lure-back-its-ai-talent/. Accessed 30 May 2025.
    [Google Scholar]
  28. Khyani Divya, et al. “An Interpretation of Lemmatization and Stemming in Natural Language Processing.” Journal of University of Shanghai for Science and Technology, vol. 22, no. 10 2021, pp 350–57.
    [Google Scholar]
  29. Lannon John M.Technical Communication. Longman 2000.
    [Google Scholar]
  30. Laquintano Timothy, Schnitzler Carly, and Vee Annette. Introduction. Text-GenEd: Teaching with Text Generation Technologies, edited byVee Annette, Laquintano Timothy, and Schnitzler Carly, WAC Clearinghouse 2023 https://doi.org/10.37514/TWR-J.2023.1.1.02.
    [Google Scholar]
  31. Latour Bruno. Pandora’s Hope: Essays on the Reality of Science Studies. Harvard UP 1999.
    [Google Scholar]
  32. Laupichler Matthias Carl, et al. “Artificial Intelligence Literacy in Higher and Adult Education: A Scoping Literature Review.” Computers and Education: Artificial Intelligence, vol. 3 2022, pp 1–15. ScienceDirect, https://doi.org/10.1016/j.caeai.2022.100101.
    [Google Scholar]
  33. Lim Fei Victor, et al. “Editorial for Special Issue: Digital Multimodal Composing in the Era of Artificial Intelligence.” Computers and Composition, vol. 75, Mar 2025 ScienceDirect, https://doi.org/10.1016/j.comp-com.2024.102911.
    [Google Scholar]
  34. Lookadoo Kathryn, and Moore Sarah. “Is Your résumé/Textbook Up-to-Date? An Audit of AI ATS résumé Instruction.” Business and Professional Communication Quarterly 2024 https://doi.org/10.1177/23294906231223101.
    [Google Scholar]
  35. Manning Christopher D., et al.Introduction to Information Retrieval. Cambridge UP 2009.
    [Google Scholar]
  36. Mayson Sandra G. “Bias In, Bias Out.” Yale Law Journal, vol. 128, no. 8 2019, pp 2218–300.
    [Google Scholar]
  37. Mechura Michal. Machine-Readable Lists of Lemma-Token Pairs in 23 Languages. https://github.com/michmech/lemmatization-lists/.
  38. Minh Dang, et al. “Explainable Artificial Intelligence: A Comprehensive Review.” Artificial Intelligence Review, vol. 55, no. 5 2021, pp 3503–68. ProQuest, https://doi.org/10.1007/s10462-021-10088-y.
    [Google Scholar]
  39. Mohan Pavithra. “70% of Companies Will Use AI for Hiring in 2025, Says New Study.” Fast Company, 31Oct 2024, https://www.fastcompany.com/91220282/70-of-companies-will-use-ai-for-hiring-in-2025-says-new-study.
    [Google Scholar]
  40. Ng Davy Tsz Kit, et al. “Conceptualizing AI Literacy: An Exploratory Review.” Computers and Education: Artificial Intelligence, vol. 2, Jan 2021 https://doi.org/10.1016/j.caeai.2021.100041.
    [Google Scholar]
  41. Noble Safiya Umoja. Algorithms of Oppression: How Search Engines Reinforce Racism. New York UP 2018.
    [Google Scholar]
  42. O’Neil Catherine. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown 2016.
    [Google Scholar]
  43. Parikh Nish. “Understanding Bias in AI-Enabled Hiring.” Forbes, 14Oct 2021, https://www.forbes.com/sites/forbe-shumanresourcescouncil/2021/10/14/understanding-bias-in-ai-enabled-hiring/?sh=42eed0d37b96.
    [Google Scholar]
  44. Pasquale Frank. The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard UP 2015.
    [Google Scholar]
  45. Plaue Matthias. Data Science: An Introduction to Statistics and Machine Learning. Springer 2023 https://doi.org/10.1007/978-3-662-67882-4.
    [Google Scholar]
  46. Pramana Rio, et al. “Systematic Literature Review of Stemming and Lemmatization Performance for Sentence Similarity.” IEEE 7th International Conference on Information Technology and Digital Applications (ICITDA), Yogyakarta, Indonesia 2022, pp 1–6, https://doi.org/10.1109/IC-ITDA55840.2022.9971451.
    [Google Scholar]
  47. Price W. Nicholson. “Big Data and Black-Box Medical Algorithms.” Science Translational Medicine, vol. 10, no. 471 2018, pp 1–3. https://doi.org/10.1126/scitranslmed.aao5333.
    [Google Scholar]
  48. Ranade Nupoor, and Eyman Douglas. “Introduction: Composing with Generative AI.” Computers and Composition, vol. 71, Mar 2024 https://doi.org/10.1016/j.compcom.2024.102834.
    [Google Scholar]
  49. Raulji Jaideepsinh K., & Saini Jatinderkumar R. “Stop-Word Removal Algorithm and Its Implementation for Sanskrit Language.” International Journal of Computer Applications, vol. 150, no. 2 2016, pp 15–17. https://doi.org/10.5120/ijca2016911462.
    [Google Scholar]
  50. RChilli. “AI résumé Parser with Accurate Data Field Extraction.” RChilli 2023, https://www.rchilli.com/solutions/résuméparser-api. Accessed 15 Dec. 2023.
    [Google Scholar]
  51. Roever Carol, and McGaughey Yvonne. “Preparing a Scannable résumé.” Business Communication Quarterly, vol. 60, no. 1 1997, pp.156–59. https://doi-org/10.1177/108056999706000114.
    [Google Scholar]
  52. Schullery Nancy M., et al. “Employer Preferences for résumés and Cover Letters.” Business Communication Quarterly, vol. 72, no. 2 2009, pp 163–76. https://doi-org/10.1177/1080569909334015.
    [Google Scholar]
  53. Silva C., & Ribeiro B. “The Importance of Stop Word Removal on Recall Values in Text Categorization.” Proceedings of the International Joint Conference on Neural Networks, 2003, Portland, OR 2003, vol 3, pp. 1661–66, https://doi.org/10.1109/IJCNN.2003.1223656.
    [Google Scholar]
  54. Tseng Waverly, and Warschauer Mark. “AI-Writing Tools in Education: If You Can’t Beat Them, Join Them.” Journal of China Computer-Assisted Language Learning, vol. 3, no. 2 2023, pp 258–62. https://doi.org/10.1515/jccall-2023-0008.
    [Google Scholar]
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