Artificial Intelligence and University Applications: Authenticity, Equity, and the Reconfiguration of Admissions Practices

 

Abstract

The integration of artificial intelligence (AI) into higher education has significantly reshaped university admissions processes. Applicants increasingly use generative AI tools such as ChatGPT to construct personal statements, while institutions deploy algorithmic systems to manage large applicant pools. This paper investigates the implications of AI-mediated admissions through a qualitative interpretivist framework. Drawing on document analysis of institutional policies, emerging empirical studies (2020–2026), and critical synthesis, the study examines how AI transforms notions of authenticity, equity, and evaluative validity. Findings indicate that AI simultaneously democratizes access to application support while destabilising traditional markers of merit. The paper argues for a shift toward process-oriented and dialogic admissions models and proposes a framework for critical AI admissions literacy. The study contributes to ongoing debates in educational technology and critical pedagogy by foregrounding the sociotechnical and ethical dimensions of AI in high-stakes selection systems.

Keywords

Artificial intelligence, university admissions, higher education, equity, authenticity, critical digital pedagogy, generative AI

1. Introduction

Historically, university admissions systems have operated as gatekeeping mechanisms, balancing meritocratic ideals with institutional priorities. These systems have traditionally relied on academic metrics, standardized testing, and qualitative components such as personal statements. The emergence of generative AI has disrupted these conventions, necessitating an examination of how established practices intersect with new technological influences.

AI-powered tools enable applicants to generate sophisticated written materials, raising critical questions regarding authorship, originality, and fairness. Concurrently, universities are implementing AI-driven systems for application screening and predictive analytics, thereby embedding algorithmic decision-making more deeply into admissions processes.

This paper addresses the central research question:

How is artificial intelligence reshaping authenticity, equity, and evaluative practices in university admissions?

 

Through a qualitative interpretivist approach, this study situates AI within broader sociotechnical transformations and emphasizes the dynamic interplay among technology, institutional practices, and human agency.

2. Literature Review

2.1 Generative AI and Academic Writing

Recent scholarship underscores the transformative impact of generative AI on academic writing practices. Evidence indicates that AI tools can improve clarity, coherence, and accessibility, particularly for students with limited academic support (Cheng et al., 2025). Nevertheless, persistent concerns regarding authorship and originality remain.

A systematic review by RSIS International (2025) found that most studies identify risks related to plagiarism, ethical misuse, and reduced critical engagement. These concerns are particularly pronounced in high-stakes contexts such as university applications, where written submissions are decisive.

2.2 AI and Admissions Practices

Institutions are increasingly incorporating AI into admissions workflows. Algorithmic systems are used to filter applications, predict student success, and optimise recruitment strategies (Marín, 2025). While these systems improve efficiency, they also introduce risks related to bias and transparency.

Research demonstrates that AI models trained on historical data can perpetuate existing inequalities, disproportionately affecting marginalized groups (Llerena-Izquierdo & Ayala-Carabajo, 2025). This situation raises critical questions regarding fairness and accountability in AI-mediated decision-making.

2.3 Authenticity and the Crisis of the Personal Statement

The personal statement has traditionally served as a medium for authentic self-expression. The proliferation of AI-generated writing, however, challenges this assumption. Cournoyea (2025) contends that the essay format is becoming increasingly unreliable as an indicator of individual capability.

Consequently, scholars recommend alternative assessment methods that prioritize process, interaction, and real-time evaluation.

2.4 Critical Digital Pedagogy

The study is grounded in critical digital pedagogy, which extends Paulo Freire's work into digital contexts. This framework emphasises:

• Power relations in technological systems
• The importance of agency and voice
• The need for ethical and equitable practice

AI in admissions should therefore be understood not only as a technical innovation but also as a sociocultural phenomenon that reshapes educational access and identity.

3. Methodology

3.1 Research Design

This study adopts a qualitative interpretivist research design, suitable for exploring complex sociotechnical phenomena. Interpretivism prioritises understanding how individuals and institutions construct meaning, making it particularly relevant for examining AI in admissions.

The research employs a critical document analysis (CDA) approach, combined with thematic synthesis, to analyse how AI is represented and operationalised in university admissions contexts.

3.2 Data Sources

The study draws on three primary data sources:

1. Institutional Policy Documents

Policies and guidance from major admissions systems, including:

• UCAS
• Common Application

These documents provide insight into official positions on AI usage.

2. Peer-Reviewed Literature (2020–2026)

A corpus of recent academic studies on:

• Generative AI in education
• AI ethics and governance
• Admissions practices and equity

Databases included Scopus, Web of Science, and Google Scholar.

3. Grey Literature

Reports, policy briefs, and preprints (e.g., arXiv) were included to capture emerging trends not yet fully represented in peer-reviewed journals.

3.3 Sampling Strategy

A purposive sampling strategy was employed to select sources that:

• Directly address AI in education or admissions.
• They are published between 2020 and 2026
• Represent diverse geographical contexts.

A total of 42 sources were included in the final dataset.

3.4 Data Analysis

Data were analysed using reflexive thematic analysis (Braun & Clarke, 2006), involving:

1. Familiarisation with data
2. Initial coding (open coding)
3. Theme development
4. Theme refinement
5. Interpretation within a critical framework

Themes were iteratively developed and reviewed to ensure coherence and analytical depth.

3.5 Trustworthiness and Rigour

To ensure rigour, the study applied:

• Credibility: Triangulation across data sources
• Dependability: Transparent documentation of methods
• Reflexivity: Acknowledgement of researcher positionality
• Transferability: Thick description of contexts

3.6 Ethical Considerations

The study relies exclusively on publicly available data and does not involve human participants. However, ethical considerations include:

• Responsible representation of institutional policies
• Critical engagement with power and bias in AI systems

4. Findings

4.1 AI as a Democratizing Tool

AI tools offer scalable support for applicants, particularly those from historically underserved groups, and have the potential to reduce persistent inequities in application preparation.

However, the findings indicate that access to AI alone does not ensure equity. Variations in AI literacy, available resources, and strategic utilization may reinforce or exacerbate existing inequities among applicants.

4.2 The Erosion of Authenticity

The widespread use of AI in writing diminishes the reliability of personal statements as indicators of individual capability. Admissions systems increasingly struggle to differentiate between human and AI-generated content.

 

4.3 Algorithmic Bias and Institutional Risk

AI systems employed in admissions can replicate historical biases embedded in training data. This situation creates several risks for institutions, including:

• Legal challenges
• Reputational damage
• Ethical violations

4.4 The Shift Toward Process-Oriented Evaluation

Institutions are beginning to move away from static written submissions toward:

• Interviews
• Portfolios
• Timed assessments

These approaches are designed to capture authentic student capabilities within environments characterized by pervasive AI use.

5. Discussion

The findings reveal a fundamental tension between efficiency and authenticity within AI-mediated admissions processes.

From a critical digital pedagogy perspective, AI both empowers and constraints:

• It expands access to resources.
• It reshapes how identity and merit are constructed.

This study contends that admissions systems should move beyond simplistic conceptions of “AI misuse” and adopt more nuanced understandings of human-AI collaboration.

6. Implications

6.1 For Policy

• Clear guidelines on ethical AI use
• Transparency in algorithmic decision-making

6.2 For Practice

• Redesign of admissions processes
• Increased use of interactive assessments

6.3 For Research

• Empirical studies on AI impact in admissions
• Focus on marginalised and neurodiverse learners.

 7. Conclusion

AI is profoundly transforming university admissions, challenging traditional assumptions regarding merit, authorship, and fairness. Although it presents opportunities for increased access and efficiency, it also introduces significant ethical and practical challenges.

The future of admissions depends on reimagining evaluation systems to align with the realities of AI-mediated learning and communication. Through a critical and reflective approach, institutions can leverage AI’s potential while safeguarding equity and authenticity.

References

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.

Cheng, A., et al. (2025). Artificial intelligence-assisted academic writing. Journal of Healthcare Simulation Research.

Cournoyea, M. (2025). Rethinking the personal statement in the AI era. Academic Medicine.

Gonsalves, C. (2025). Addressing student non-compliance in AI use declarations. Assessment & Evaluation in Higher Education.

Jeon, J. (2025). The ethics of generative AI in social science research. Technology in Society.

Lee, J., Borchers, C., Alvero, A. J., Joachims, T., & Kizilcec, R. F. (2026). The digital divide in generative AI. arXiv preprint.

Llerena-Izquierdo, J., & Ayala-Carabajo, R. (2025). Ethics of AI in academia. Informatics, 12(4), 111.

Marín, Y. R. (2025). Ethical challenges associated with AI in universities. Journal of Academic Ethics.

RSIS International. (2025). Ethical use of AI in academic writing: A systematic review.