Leveraging Artificial Intelligence for Enhancing Campus Placement Outcomes in Rajasthan's Deemed-to-be-Universities

DRA ANNUAL INTERNATIONAL CONFERENCE 2024 ON “SOCIO-ECONOMIC TRANSFORMATION: OPPORTUNITIES AND CHALLENGES” Int. Jr. of Contemp. Res. in Multi. PEER-REVIEWED JOURNAL Volume 4 [Special Issue 1] Year 2025 68 © 2025 Kiran Chopra. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY NC ND).https://creativecommons.org/licenses/by/4.0/ Conference Paper Leveraging Artificial Intelligence for Enhancing Campus Placement Outcomes in Rajasthan's Deemed-to-be-Universities Kiran Chopra * Research Scholar, IIS University, Jaipur, Rajasthan, India Corresponding Author: *Kiran Chopra DOI: https://doi.org/10.5281/zenodo.17942346 Abstract Manuscript Information Background: This study focuses on AI's ability to automate recruitment tasks, enhancing efficiency and decision-making in talent acquisition, particularly in the context of campus placements at universities. Objectives: It aims to explore the use of AI in campus placements within deemed-to-be universities in Rajasthan, examine the challenges faced by these institutions, and evaluate the role AI can play in improving their recruitment processes. Methodology: The research methodology employs a quantitative approach using a structured survey distributed to 60 recruiting organisations, targeting HR professionals through purposive sampling to gather data on AI's challenges and benefits in campus hiring. Key Findings: This study found that AI improves fairness, precision, and candidate-job matching in campus hiring but faces challenges such as evaluating soft skills, high implementation costs, and integration issues with existing HR systems. Conclusion: This study concludes that while AI offers significant potential to streamline and enhance campus hiring processes in deemed-to-be universities in Rajasthan, technical and humancentric barriers limit its full adoption. Implications: This study suggests that integrating AI with traditional HR practices can optimise recruitment outcomes, but organisations must address technical challenges like data requirements and implementation costs for AI to be fully effective in campus hiring. ▪ ISSN No: 2583-7397 ▪ Received: 12-12-2024 ▪ Accepted: 23-02-2025 ▪ Published: 18-03-2025 ▪ IJCRM:4(SP1); 2025: 68-73 ▪ ©2025, All Rights Reserved ▪ Plagiarism Checked: Yes ▪ Peer Review Process: Yes How to Cite this Article Chopra K. Leveraging Artificial Intelligence for Enhancing Campus Placement Outcomes in Rajasthan's Deemed-to-beUniversities. Int J Contemp Res Multidiscip. 2025;4(SP1):68-73. Access this Article Online www.multiarticlesjournal.com KEYWORDS: Campus Placement, HR Hiring, Recruitment Process, Artificial Intelligence, Applications of AI. DRA ANNUAL INTERNATIONAL CONFERENCE 2024 ON “SOCIO-ECONOMIC TRANSFORMATION: OPPORTUNITIES AND CHALLENGES” Int. Jr. of Contemp. Res. in Multi. PEER-REVIEWED JOURNAL Volume 4 [Special Issue 1] Year 2025 69 © 2025 Kiran Chopra. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY NC ND).https://creativecommons.org/licenses/by/4.0/ 1. INTRODUCTION In the digital age, technology has dramatically reshaped every field, and human resource management is no exception. Today, businesses operate in a highly competitive environment that requires innovation and efficient operations. Human Resources (HR) departments in particular face the challenge of identifying and recruiting the right talent for organisational success. This is especially true in higher education institutions, where campus recruiting plays a key role in attracting new talent. Incorporating advanced technologies such as artificial intelligence (AI) into the recruitment process offers new opportunities to increase efficiency and streamline talent acquisition, making it more effective in identifying highperforming candidates that match the specific needs of an organisation (Seetha Lakshmi et al., 2020) [6]. AI has become a valuable tool for making HR processes more efficient, especially when it comes to talent acquisition. By leveraging AI, HR professionals can now automate a range of recruiting tasks, including candidate sourcing, screening, and matching. This shift enables organisations to make faster, more informed decisions about potential employees, thereby improving the quality of candidates hired (Paramita et al., 2024) [5]. AI-driven systems can effectively pool and evaluate large numbers of candidates, ensuring that only those with the right qualifications and capabilities are shortlisted for further consideration (Jia et al., 2018) [3]. This capability is highly relevant to universities’ campus recruitment processes, where a large number of graduates seek employment opportunities every year. AI in campus recruiting solves key HR challenges by automating time-consuming tasks and reducing bias in decision-making. It provides data-driven insights that make recruiting more objective while enhancing the candidate experience through effective communication and feedback (Jia et al., 2018) [3]. For deemed-to-be-universities in Rajasthan, AI can transform campus recruiting, helping institutions match the right talent with the right positions in a competitive market. However, adoption remains limited, with only 22% of organisations using AI, indicating challenges in integration and implementation (Tambe et al., 2019) [7]. This research was motivated by the need for deemed-to-be-universities in Rajasthan to improve their campus recruitment process in a competitive environment. Traditional methods are often inefficient and biased, making it more difficult to select quality candidates. Given the potential of AI to streamline the recruitment process and improve decision-making, this research article aims to explore the use of AI in campus recruitment at deemed-to-be-universities in Rajasthan, address specific challenges faced by these institutions, and examine the role that AI can play in improving the overall recruitment process. 2. RESEARCH METHODOLOGY Research Design: This study employs a quantitative research design, using a structured survey questionnaire to gather data from recruiting organisations about their use of AI in campus hiring. Study Population The study focuses on recruiting agencies and organisations involved in campus placements at deemed-to-be universities in Rajasthan. Sampling Units The sampling units are HR managers and talent acquisition professionals from Tech service companies, non-tech companies, product companies, MNCs, Startups, and core companies that use or are interested in using AI for recruitment. Sampling Techniques A purposive sampling technique was used to select organisations with relevant experience in AI-based recruitment processes. Sample Size The sample consists of 60 recruiting organisations, ensuring enough data to derive meaningful insights while being manageable for analysis. Data Collection Tool A structured questionnaire was developed and distributed to collect data on the challenges and benefits of AI in campus hiring, using an online survey hosted on the Qualtrics Survey platform. Description of Questionnaire The questionnaire is divided into three parts: demographic information (5 items), challenges of AI adoption in recruitment (10 items), and the role of AI in enhancing campus placements (10 items), using a 5-point Likert scale. Data Collection Process The survey was conducted over two months (July and August 2024), with follow-up reminders sent to ensure a sufficient response rate from the target organisations. DRA ANNUAL INTERNATIONAL CONFERENCE 2024 ON “SOCIO-ECONOMIC TRANSFORMATION: OPPORTUNITIES AND CHALLENGES” Int. Jr. of Contemp. Res. in Multi. PEER-REVIEWED JOURNAL Volume 4 [Special Issue 1] Year 2025 70 © 2025 Kiran Chopra. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY NC ND).https://creativecommons.org/licenses/by/4.0/ Data Analysis Tools The data were analysed using the Statistical Package for Social Sciences (SPSS) version 23.0. Descriptive and inferential statistical tools, such as frequencies, percentages, mean, standard deviation, and t-score, were used to analyse the data collected. Ethical Consideration Participation was voluntary, and respondents' anonymity and confidentiality were ensured, with data used solely for research purposes. 3. ANALYSIS AND FINDINGS Table 1: Analysis of Demographic Information Demographic Components (N=60) Frequency Percent Nature of Organisation IT/ Tech Service Companies 18 30.0 Non-tech Companies 10 16.7 Product Companies 8 13.3 MNCs 6 10.0 Startups 12 20.0 Core Companies 6 10.0 Size of Organisation 1-25 7 11.7 26-50 13 21.7 51-75 25 41.7 More than 75 15 25.0 Department of Interest Arts, Humanities, & Social Sciences 3 5.0 Engineering & Architecture 16 26.7 Technology & Computer Applications 27 45.0 Commerce & Management 9 15.0 Others 5 8.3 Experience in Using AI for Recruitment 0-2 years 13 21.7 2-4 years 31 51.7 More than 4 years 16 26.7 AI Recruitment Software Preferred HireVue 6 10.0 SeekOut 11 18.3 Pymetrics 4 6.7 Recruitee 9 15.0 TurboHire 14 23.3 Humanly 5 8.3 Others 11 18.3 Explanation: The table presents demographic data from a group of 60 participants categorised based on several factors related to their organisations and experience with AI recruitment software. It begins by detailing the nature of the organisations, with 30% coming from IT/tech services, followed by 20% from startups, while the smallest groups include MNCs and core companies at 10% each. The size of these organisations varies, with 41.7% having between 51 and 75 employees, while only 11.7% have fewer than 25 employees. In terms of the departments of interest, the majority (45%) are from technology and computer applications, followed by 26.7% in engineering and architecture. Experience with AI in recruitment shows that over half (51.7%) have 2-4 years of experience, while 26.7% have more than 4 years. When it comes to AI recruitment software, TurboHire is the most preferred, used by 23.3%, while 18.3% opt for either SeekOut or other tools. Interpretation: The data shows a high representation of the IT and tech sectors, suggesting that organisations in these industries are more likely to integrate AI into the recruitment process. The prevalence of mid-sized organisations means that AI recruitment tools may be more beneficial or more accessible to companies that are neither large nor small. The technology and computer application industries showed the highest interest, which is consistent with the highly technical nature of the respondent group. The mediocre experience level of using AI for recruitment indicates that AI adoption is relatively new, but has grown significantly in recent years. TurboHire emerged as the most popular software, perhaps reflecting its suitability for the needs of participants, although there was also a large difference in the tools used, indicating that the market for AI recruitment platforms is highly competitive. DRA ANNUAL INTERNATIONAL CONFERENCE 2024 ON “SOCIO-ECONOMIC TRANSFORMATION: OPPORTUNITIES AND CHALLENGES” Int. Jr. of Contemp. Res. in Multi. PEER-REVIEWED JOURNAL Volume 4 [Special Issue 1] Year 2025 71 © 2025 Kiran Chopra. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY NC ND).https://creativecommons.org/licenses/by/4.0/ Table 2: Analysis of Specific Challenges faced in using AI in Campus Hiring Specific Challenges (N=60) Mean SD SE of Mean t-score p-value The cost of AI implementation is too high for the organisation 3.83 0.85 0.11 34.60 .000 Lack of technical expertise to manage and maintain AI systems 3.67 0.80 0.10 35.22 .000 Data privacy and security concerns prevent widespread use of AI in recruitment 3.58 0.74 0.10 36.83 .000 AI-based recruitment tools do not always integrate well with existing HR systems 3.80 0.71 0.09 41.01 .000 The accuracy of AI in evaluating soft skills and cultural fit is still limited 4.02 0.65 0.08 47.22 .000 There is resistance from HR teams to fully adopt AI in recruitment processes 3.75 0.73 0.09 39.38 .000 AI-based recruitment solutions require a significant amount of data to function effectively, which is challenging to provide 4.00 0.76 0.10 40.31 .000 AI tools can sometimes reinforce biases present in historical data, leading to biased recruitment outcomes 3.77 0.67 0.09 42.77 .000 AI lacks the human touch needed in recruitment, which affects candidate engagement and experience 3.93 0.63 0.08 47.43 .000 Regulatory and compliance issues limit the organisation's ability to fully adopt AI in recruitment 3.65 0.86 0.11 32.42 .000 Explanation: The table summarises the specific challenges faced in using AI for campus hiring, based on responses from 60 participants. The challenges are rated on a scale, with higher mean scores indicating greater difficulty. The challenge with the highest mean is the limited accuracy of AI in evaluating soft skills and cultural fit (M=4.02, SD=0.65), followed by the significant data requirements for AI tools to function effectively (M=4.00, SD=0.76). The lowest-rated challenge is data privacy and security concerns (M=3.58, SD=0.74). All challenges have a p-value of .000, indicating that they are statistically significant. The relatively low standard errors suggest consistency in responses across the sample. Interpretation: The data highlights the specific barriers to AI adoption in campus recruiting, with concerns about the accuracy of AI in assessing soft skills and cultural fit being the most prominent. This suggests that while AI may be effective at technical assessments, it struggles with the subjective aspects of recruiting. Similarly, the challenge of requiring large data sets for AI to work effectively may reflect the difficulties smaller organisations face when leveraging AI tools. Cost, integration issues, and resistance from HR teams also ranked high, meaning that while AI is seen as a powerful tool, its implementation is not seamless. Notably, issues such as privacy, regulatory concerns, and bias, while important, are less pressing, which may indicate that trust in AI compliance and fairness has increased over time. Overall, these challenges reflect both technical and human-centred barriers to fully integrating AI into the recruiting process. Table 3: Analysis of the Role of AI in Improving Placements Role of AI (N=60) Mean SD SE of Mean t-score p-value AI reduces bias in the recruitment process by focusing solely on data-driven insights 3.88 0.72 0.09 41.51 .000 AI helps identify the most suitable candidates quickly and efficiently during campus recruitment 3.33 0.90 0.12 28.39 .000 AI improves the quality of hires by assessing a broader set of skills and qualifications 3.52 0.91 0.12 29.47 .000 Using AI in campus recruitment helps improve employer branding by demonstrating innovation and efficiency 3.32 0.79 0.10 31.96 .000 AI enables more accurate matching of candidate profiles to specific job roles during campus recruitment 4.00 0.84 0.11 36.26 .000 AI shortens the recruitment cycle, allowing organisations to make faster hiring decisions in campus placements 3.73 0.90 0.12 31.72 .000 AI helps in filtering a large pool of candidates more effectively, which is crucial in campus recruitment 3.43 0.77 0.10 34.16 .000 The use of AI in campus recruitment ensures fairness and transparency throughout the hiring process 3.65 0.78 0.10 35.87 .000 AI-based recruitment tools enhance the candidate experience by providing timely communication and feedback 3.93 0.86 0.11 34.94 .000 AI improves collaboration between the HR department and hiring managers by offering more precise candidate recommendations 3.75 0.70 0.09 40.70 .000 Explanation: The table presents data on the role of AI in improving placements during campus recruitment, based on responses from 60 participants. The highest mean score of 4.00 is for AI’s ability to accurately match candidate profiles to specific job roles, indicating strong agreement on this point. Other notable roles include reducing bias (M=3.88, SD=0.72), improving candidate experience (M=3.93, SD=0.86), and enhancing HR collaboration (M=3.75, SD=0.70). On the lower end, participants rated AI's role in improving employer branding (M=3.32, SD=0.79) and identifying suitable candidates quickly (M=3.33, SD=0.90) with lower scores. All challenges have a statistically significant p-value of .000, and standard errors indicate consistent responses across the sample. Interpretation: Data shows that AI is valued in campus recruitment for accurately matching job seekers and positions and improving fairness. However, its effectiveness in quickly identifying candidates and enhancing the employer brand is considered less important, indicating that there is room for improvement. While AI's role in reducing bias and improving DRA ANNUAL INTERNATIONAL CONFERENCE 2024 ON “SOCIO-ECONOMIC TRANSFORMATION: OPPORTUNITIES AND CHALLENGES” Int. Jr. of Contemp. Res. in Multi. PEER-REVIEWED JOURNAL Volume 4 [Special Issue 1] Year 2025 72 © 2025 Kiran Chopra. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY NC ND).https://creativecommons.org/licenses/by/4.0/ transparency is recognised, speed and brand scores are low, indicating that human involvement is still key in some aspects of recruitment. Overall, AI has improved accuracy and fairness but has not yet completely replaced traditional methods in candidate identification and employer perception. 4. DISCUSSION The analysis highlights several key insights into the use of AI in campus recruiting. Organisations, especially those in the IT and tech industries, are increasingly adopting AI in the recruitment process. However, significant challenges remain, such as AI's limited ability to assess soft skills and cultural fit, high implementation costs, and concerns about data requirements for AI to function effectively. Despite these obstacles, AI is recognised for its potential to improve recruiting information by increasing fairness, reducing bias, and providing accurate candidate-job matches. However, its impact on accelerating hiring and enhancing the employer brand is considered less effective. Overall, while AI is seen as a valuable tool for achieving precision and transparency, its integration faces both technical and human-centred barriers. These findings suggest that AI plays a nuanced role in campus recruiting, which echoes trends from earlier research. As noted by Bogen and Rieke (2018) [1], AI is often viewed as a promising solution to reduce bias in hiring by relying on datadriven decision-making, and the high mean scores for AI’s ability to reduce bias in this study support this view. However, consistent with Upadhyay and Khandelwal (2018) [8], AI still faces significant challenges in accurately assessing soft skills and cultural fit. This is highlighted in the current data, with respondents citing AI’s accuracy in assessing these aspects as a major limitation. The issues of AI’s high cost and large data requirements noted by Tambe et al. (2019) [7] are reflected in the current findings, with respondents expressing concerns about implementation costs and the large datasets required to use AI effectively. While AI’s ability to match candidate profiles to positions is viewed as its greatest strength, consistent with the findings of Faliagka et al. (2012) [2] on the success of AI in matching candidates and positions, AI’s impact on faster hiring and improved employer branding scored relatively low, suggesting that the human element in recruitment remains critical, as also discussed by Lee (2018) [4]. Resistance from HR teams and concerns about the integration of AI with existing HR systems further highlight the need for a more seamless approach, as noted in previous research (Jia et al., 2018) [3]. Thus, while AI has the potential to improve fairness and accuracy in recruitment, the existing literature confirms that there are still significant barriers that need to be addressed to realise its full potential in campus recruitments (Paramita et al., 2024; Seetha Lakshmi et al., 2020) [5, 6]. 5. CONCLUSION This study shows that despite AI's significant benefits in improving the fairness, accuracy, and efficiency of campus recruiting, several challenges remain. Organisations, especially those in the IT and tech industries, have embraced AI tools for recruiting, recognising their ability to reduce bias and more accurately match candidates to positions. However, limitations in assessing soft skills, high implementation costs, data privacy concerns, and integration issues with existing HR systems pose significant barriers. Despite these challenges, AI is still considered a valuable tool, although its full potential has yet to be realised, especially in accelerating recruiting and strengthening employer branding. This study highlights the importance of integrating AI with traditional HR practices to improve hiring outcomes and provides insights into how AI can optimise candidate selection while ensuring fairness and transparency. The findings highlight the need for organisations to address technical and operational challenges, such as AI’s limited ability to assess soft skills and high implementation costs. However, a limitation of this study is that it focused only on organisations in Rajasthan, which may not reflect broader trends across different regions or industries. Additionally, reliance on self-reported data from participants could introduce response bias, limiting the general applicability of the findings. Future research could explore developing AI tools that can more effectively assess soft skills and cultural fit, which remain challenging with current systems. It would also be valuable to study how smaller organisations overcome data and cost barriers to implement AI in recruiting. Additionally, future research could expand to a geographic scope to assess the use of AI in recruiting across different regions and industries, providing a more complete picture of AI adoption. Longitudinal research could also track how the impact of AI on recruiting practices changes over time, providing more insight into its long-term effectiveness and sustainability. REFERENCES 1. Bogen M, Rieke A. Help Wanted: An Examination of Hiring Algorithms, Equity, and Bias. Upturn; 2018. Available from: https://www.upturn.org/reports/2018/hiring-algorithms/ DRA ANNUAL INTERNATIONAL CONFERENCE 2024 ON “SOCIO-ECONOMIC TRANSFORMATION: OPPORTUNITIES AND CHALLENGES” Int. Jr. of Contemp. Res. in Multi. PEER-REVIEWED JOURNAL Volume 4 [Special Issue 1] Year 2025 73 © 2025 Kiran Chopra. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY NC ND).https://creativecommons.org/licenses/by/4.0/ 2. Faliagka E, Tsakalidis A, Tzimas G. An integrated erecruitment system for automated personality mining and applicant ranking. Internet Research. 2012;22(5):551-568. 3. Jia N, Guo R, He L. Challenges and benefits of implementing AI in human resource management. Journal of Business Research. 2018;95:160-169. 4. Lee I. Social media analytics for enterprises: Typology, methods, and processes. Business Horizons. 2018;61(2):199-210. 5. Paramita D, Okwir S, Nuur C. Artificial intelligence in talent acquisition: exploring organisational and operational dimensions. International Journal of Organisational Analysis. 2024;32(11):108-131. 6. Seetha Lakshmi R, Sowdamini T, Biswas AK. The rise of artificial intelligence in talent acquisition. Perspectives on Business Management & Economics. 2020;III:161-164. 7. Tambe P, Cappelli P, Yakubovich V. Artificial intelligence in human resources management: Challenges and a path forward. California Management Review. 2019;61(4):1542. 8. Upadhyay A, Khandelwal K. Artificial intelligence-based recruitment and selection: A case of Indian IT industry. Strategic HR Review. 2018;17(5):255-258. Creative Commons (CC) License This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY 4.0) license. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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