Home / Current Issue / Paper 1724873
AI Mock Interview System: A Web-Based Framework for Structured Interview Practice and Explainable Answer Assessment
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
DOI: 10.64388/ire1724873
Abstract
AI Mock Interview System is a web-based platform designed to help students and job seekers practise technical and human-resources interviews in an organized way. Interview preparation needs repeated practice, relevant questions, and feedback that explains how an answer can be improved, but candidates do not always have a mentor who can provide this. The current application provides role selection, interview-category configuration, question presentation, session management, and a results interface using frontend web technologies, Java Spring Boot REST services, and MySQL for relevant application data. A clear distinction is made between this existing workflow and an AI assessment capability, which has not yet been verified in the implementation. To go beyond interface integration, the paper proposes a rubric-guided answer-assessment module that evaluates technical correctness, relevance, completeness, and clarity, and returns feedback linked to evidence in the response. The proposed evaluation compares automated scores with independent human ratings and with baseline approaches using error, agreement, and robustness measures. No experimental accuracy or effectiveness result is claimed, because the assessment component, dataset, and tests remain to be completed. Overall, the paper documents the problem, system architecture, implementation considerations, evaluation plan, limitations, and future work.
Keywords
AI-assisted interview practice; automated answer assessment; explainable AI; semantic analysis; rubric-based scoring; personalized feedback
References
[1] Burrows S, Gurevych I, Stein B. The eras and trends of automatic short answer grading. Int J Artif Intell Educ. 2015;25:60–117. https://doi.org/10.1007/s40593-014-0026-8
[2] Mohler M, Mihalcea R. Text-to-text semantic similarity for automatic short answer grading. In: Proc. 12th Conf. European Chapter of the ACL (EACL); 2009. p. 567–575. https://doi.org/10.3115/1609067.1609130
[3] Liff J, Mondragon N, Gardner C, Hartwell CJ, Bradshaw A. Psychometric properties of automated video interview competency assessments. J Appl Psychol. 2024;109(6):921–948. https://doi.org/10.1037/apl0001173
[4] Hickman L, Liff J, Rottman C, Calderwood C, et al. The effects of the training sample size, ground truth reliability, and NLP method on language-based automatic interview scores’ psychometric properties. Organ Res Methods. 2024. https://doi.org/10.1177/10944281241264027
[5] Szandała T. ChatGPT vs human expertise in the context of IT recruitment. Expert Syst Appl. 2025;264:125868. https://doi.org/10.1016/j.eswa.2024.125868
[6] del Gobbo E, Guarino A, Cafarelli B, Grilli L. GradeAid: a framework for automatic short answers grading in educational contexts—design, implementation and evaluation. Knowl Inf Syst. 2023;65:4295–4334. https://doi.org/10.1007/s10115-023-01892-9
[7] Gombert S, et al. Are rubrics all you need? Towards rubric-based automatic short answer scoring via guided rubric-answer alignment. In: Proc. 16th Int. Learning Analytics and Knowledge Conf. (LAK ’26); 2026. p. 272–282. https://doi.org/10.1145/3785022.3785064
[8] Hickman L, Tay L, Woo SE. Are automated video interviews smart enough? Behavioral modes, reliability, validity, and bias of machine learning cognitive ability assessments. J Appl Psychol. 2025;110(3):314–335. https://doi.org/10.1037/apl0001236
How to cite this paper
@article{1724873,
author = {Mohammed Nazeer Ahmed, N. Akshith, NB. Yashwanth, K. Barath, Dr. Dharmendra Roy},
title = {AI Mock Interview System: A Web-Based Framework for Structured Interview Practice and Explainable Answer Assessment},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {5},
pages = {1574-1583},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724873.pdf},
abstract = {AI Mock Interview System is a web-based platform designed to help students and job seekers practise technical and human-resources interviews in an organized way. Interview preparation needs repeated practice, relevant questions, and feedback that explains how an answer can be improved, but candidates do not always have a mentor who can provide this. The current application provides role selection, interview-category configuration, question presentation, session management, and a results interface using frontend web technologies, Java Spring Boot REST services, and MySQL for relevant application data. A clear distinction is made between this existing workflow and an AI assessment capability, which has not yet been verified in the implementation. To go beyond interface integration, the paper proposes a rubric-guided answer-assessment module that evaluates technical correctness, relevance, completeness, and clarity, and returns feedback linked to evidence in the response. The proposed evaluation compares automated scores with independent human ratings and with baseline approaches using error, agreement, and robustness measures. No experimental accuracy or effectiveness result is claimed, because the assessment component, dataset, and tests remain to be completed. Overall, the paper documents the problem, system architecture, implementation considerations, evaluation plan, limitations, and future work.},
keywords = {AI-assisted interview practice; automated answer assessment; explainable AI; semantic analysis; rubric-based scoring; personalized feedback},
month = {November},
doi = {https://doi.org/10.64388/ire1724873}
}