AI in Credential Evaluation: Promise, Risks, and Responsible Use
Dev Srivastava
Aug 20, 2025
This is the second installment in our three-part series on AI in credential evaluation. While the first post outlined the challenges and Trential’s approach, here we turn to the debate itself—examining both the promises and the concerns that shape how AI should be used responsibly in qualification recognition and equivalency.
Introduction
The recognition of academic qualifications and credentials has become a cornerstone of global mobility. Universities, employers, and professional bodies are confronted with increasing volumes of applications that must be processed efficiently, consistently, and fairly. This demand places considerable strain on admissions offices and evaluation agencies, which are tasked with navigating a wide diversity of document formats, languages, and educational systems.
Against this backdrop, artificial intelligence (AI) has been proposed as a means of improving speed and scale in credential evaluation. Yet much of the discussion remains clouded by hype, treating AI as a monolithic solution rather than a set of technologies with concrete capabilities and clear limitations. This risks overstating (or understating) what AI can achieve and underestimating the importance of human judgment in high-stakes decisions.
The purpose of this article is to take a measured view of AI in credential evaluation and recognition. Rather than presenting AI as a shortcut or a substitute for expertise, we examine how it can realistically contribute to current workflows: automating routine tasks, ensuring consistency, and flagging ambiguities for further human review. Equally important, we explore the risks and limitations of relying on AI systems, from concerns over transparency and bias to questions of accountability and data protection.
By grounding the discussion in real-world operations, we aim to move beyond buzzwords and toward a more practical understanding: where AI adds value, where it falls short, and how a balanced, human-in-the-loop approach can support better outcomes in international education and professional recognition.
This is the second installment in our three-part series on AI in credential evaluation. While the first post outlined the challenges and Trential’s approach, here we turn to the debate itself—examining both the promises and the concerns that shape how AI should be used responsibly in qualification recognition and equivalency.
Introduction
The recognition of academic qualifications and credentials has become a cornerstone of global mobility. Universities, employers, and professional bodies are confronted with increasing volumes of applications that must be processed efficiently, consistently, and fairly. This demand places considerable strain on admissions offices and evaluation agencies, which are tasked with navigating a wide diversity of document formats, languages, and educational systems.
Against this backdrop, artificial intelligence (AI) has been proposed as a means of improving speed and scale in credential evaluation. Yet much of the discussion remains clouded by hype, treating AI as a monolithic solution rather than a set of technologies with concrete capabilities and clear limitations. This risks overstating (or understating) what AI can achieve and underestimating the importance of human judgment in high-stakes decisions.
The purpose of this article is to take a measured view of AI in credential evaluation and recognition. Rather than presenting AI as a shortcut or a substitute for expertise, we examine how it can realistically contribute to current workflows: automating routine tasks, ensuring consistency, and flagging ambiguities for further human review. Equally important, we explore the risks and limitations of relying on AI systems, from concerns over transparency and bias to questions of accountability and data protection.
By grounding the discussion in real-world operations, we aim to move beyond buzzwords and toward a more practical understanding: where AI adds value, where it falls short, and how a balanced, human-in-the-loop approach can support better outcomes in international education and professional recognition.
The Promise of AI
AI’s strongest contributions in credential evaluation lie not in replacing human judgment but in automating routine and repetitive processes that currently consume significant institutional resources. It can reduce the clerical burden on evaluators and create space for human experts to focus on tasks where contextual understanding and nuanced decision-making are indispensable.
Classification and Pre-processing
One of the earliest tasks in any evaluation process is sorting documents: distinguishing transcripts from diplomas, certificates, or supporting identity materials. Machine learning models trained on document formats and layouts with multi-lingual capabilities can perform this classification reliably at scale, reducing manual sorting effort. This is particularly valuable for institutions that handle thousands of multi-document applications annually.
Data Structuring: OCR, NLP, and Translation
Many applicant documents arrive in unstructured or semi-structured formats—scanned images, PDFs, or handwritten annotations. Optical character recognition (OCR) combined with natural language processing (NLP) allows AI systems to extract and analyze text and normalize it into structured fields (degree type, issuing institution, graduation date, GPA). This structured data can then be searched, compared, and integrated with internal workflows.
Beyond that, AI can support course-by-course evaluation. Models can detect the grading scale used in a transcript (e.g., 10-point vs. 4-point systems), identify course codes and subjects, and suggest equivalency mappings to local grading frameworks. When documents are submitted in multiple languages, neural machine translation provides context-aware translations that are faster and more consistent than traditional approaches, giving evaluators an accessible baseline for review.
Eligibility Screening
Credential evaluation often begins with threshold checks: minimum GPA requirements, the presence of prerequisite degrees, or recognition of the issuing institution. Once data has been structured, AI systems can automatically flag candidates who meet or fail these baseline criteria. Borderline or ambiguous cases can be marked for human review, ensuring automation supports efficiency without replacing oversight.
Accreditation verification is a central part of this process. Here, AI can cross-reference the issuing institution against trusted databases and accreditation registries. Retrieval-augmented generation (RAG) systems, for instance, can pull the most relevant accreditation information from authoritative sources, providing evaluators with both the result and the supporting evidence. This reduces the risk of oversight while keeping the evaluator in control of the final judgment.
Fraud and Anomaly Detection
The detection of fraudulent or altered documents is a growing concern. AI models trained on authentic credential templates can flag inconsistencies in document layout, typography, or seal placement. Similarly, anomaly detection techniques can identify irregularities in reported grades, credit hours, or date sequences that merit closer human inspection. While no system is foolproof, such automated checks add an additional layer of protection against fraud and can be deployed at scale more consistently than manual review.
Consistency at Scale
Perhaps the most underappreciated advantage of AI is its ability to apply the same logic to thousands of cases. Unlike human evaluators, who may interpret criteria differently depending on experience or fatigue, AI systems maintain uniformity in data extraction, classification, and eligibility screening. This consistency does not replace human interpretation, but it reduces the variability that often complicates comparative assessments.
Efficiency Gains
Taken together, these capabilities deliver measurable gains in efficiency. Tasks such as document sorting, field extraction, and basic eligibility screening can be automated to free teams from repetitive clerical work. Instead, human expertise can be concentrated on the more complex aspects of evaluation—assessing equivalency standards to be used, interpreting non-standard qualifications, and providing the contextual judgment that AI cannot replicate. The result is a workflow that is faster, more consistent, and potentially more accurate, not because AI replaces expertise but because it amplifies its reach.
The Promise of AI
AI’s strongest contributions in credential evaluation lie not in replacing human judgment but in automating routine and repetitive processes that currently consume significant institutional resources. It can reduce the clerical burden on evaluators and create space for human experts to focus on tasks where contextual understanding and nuanced decision-making are indispensable.
Classification and Pre-processing
One of the earliest tasks in any evaluation process is sorting documents: distinguishing transcripts from diplomas, certificates, or supporting identity materials. Machine learning models trained on document formats and layouts with multi-lingual capabilities can perform this classification reliably at scale, reducing manual sorting effort. This is particularly valuable for institutions that handle thousands of multi-document applications annually.
Data Structuring: OCR, NLP, and Translation
Many applicant documents arrive in unstructured or semi-structured formats—scanned images, PDFs, or handwritten annotations. Optical character recognition (OCR) combined with natural language processing (NLP) allows AI systems to extract and analyze text and normalize it into structured fields (degree type, issuing institution, graduation date, GPA). This structured data can then be searched, compared, and integrated with internal workflows.
Beyond that, AI can support course-by-course evaluation. Models can detect the grading scale used in a transcript (e.g., 10-point vs. 4-point systems), identify course codes and subjects, and suggest equivalency mappings to local grading frameworks. When documents are submitted in multiple languages, neural machine translation provides context-aware translations that are faster and more consistent than traditional approaches, giving evaluators an accessible baseline for review.
Eligibility Screening
Credential evaluation often begins with threshold checks: minimum GPA requirements, the presence of prerequisite degrees, or recognition of the issuing institution. Once data has been structured, AI systems can automatically flag candidates who meet or fail these baseline criteria. Borderline or ambiguous cases can be marked for human review, ensuring automation supports efficiency without replacing oversight.
Accreditation verification is a central part of this process. Here, AI can cross-reference the issuing institution against trusted databases and accreditation registries. Retrieval-augmented generation (RAG) systems, for instance, can pull the most relevant accreditation information from authoritative sources, providing evaluators with both the result and the supporting evidence. This reduces the risk of oversight while keeping the evaluator in control of the final judgment.
Fraud and Anomaly Detection
The detection of fraudulent or altered documents is a growing concern. AI models trained on authentic credential templates can flag inconsistencies in document layout, typography, or seal placement. Similarly, anomaly detection techniques can identify irregularities in reported grades, credit hours, or date sequences that merit closer human inspection. While no system is foolproof, such automated checks add an additional layer of protection against fraud and can be deployed at scale more consistently than manual review.
Consistency at Scale
Perhaps the most underappreciated advantage of AI is its ability to apply the same logic to thousands of cases. Unlike human evaluators, who may interpret criteria differently depending on experience or fatigue, AI systems maintain uniformity in data extraction, classification, and eligibility screening. This consistency does not replace human interpretation, but it reduces the variability that often complicates comparative assessments.
Efficiency Gains
Taken together, these capabilities deliver measurable gains in efficiency. Tasks such as document sorting, field extraction, and basic eligibility screening can be automated to free teams from repetitive clerical work. Instead, human expertise can be concentrated on the more complex aspects of evaluation—assessing equivalency standards to be used, interpreting non-standard qualifications, and providing the contextual judgment that AI cannot replicate. The result is a workflow that is faster, more consistent, and potentially more accurate, not because AI replaces expertise but because it amplifies its reach.
Risks and Limitations
While the promise of AI in credential evaluation is significant, its deployment raises several risks and limitations that institutions must account for. These do not negate the benefits outlined earlier, but they highlight the importance of a careful judgement of any automation project.
Opaque Black Boxes
AI models often function as “black boxes,” making it hard to understand how outputs are generated. In credential evaluation, this can obscure why a document is flagged or an equivalency suggested, complicating accountability and the detection of hidden biases. Opacity underscores the need for explainable systems and human review.
Contextual Understanding
Credential evaluation often requires nuanced interpretation of institutional practices, grading scales, or national education systems. AI can extract and structure data points, but it cannot easily determine, for instance, whether a three-year degree from one system equates to a four-year degree elsewhere. Over-reliance on AI in such cases risks oversimplification and inaccurate equivalency judgments.
Overconfidence and “Automation Bias”
One subtle but significant risk is automation bias—the human tendency to over-trust machine outputs, even when they are flawed. If teams treat AI-generated equivalencies authoritative rather than provisional, errors may propagate through the evaluation process unchecked. This risk is magnified when systems present outputs without transparent confidence levels or without explanations for how conclusions were reached.
Bias and Representation
AI models are trained on data, and if the training data does not adequately reflect global diversity in qualifications, institutional formats, or languages, the system may underperform on certain regions or groups. This creates a risk of structural bias, where applicants from underrepresented systems face disproportionately higher error rates or are flagged as anomalies simply because their documentation differs from the training norm.
Fraud and Adversarial Manipulation
AI can be a tool against fraud, but it can also be manipulated. Synthetic document generation and adversarial modifications (e.g., injections to instruct LLMs) present new challenges. Institutions that rely too heavily on automation risk falling into a cycle of escalation between fraudsters and fraud detection algorithms.
Cost-Benefit Misalignment
While AI promises efficiency, implementation is not without cost—both financial and organizational. Institutions need to balance the investment in AI infrastructure, training, and oversight against the actual gains in efficiency. In some contexts, partial automation or hybrid systems may yield more practical results than attempting to automate end-to-end evaluation.
Risks and Limitations
While the promise of AI in credential evaluation is significant, its deployment raises several risks and limitations that institutions must account for. These do not negate the benefits outlined earlier, but they highlight the importance of a careful judgement of any automation project.
Opaque Black Boxes
AI models often function as “black boxes,” making it hard to understand how outputs are generated. In credential evaluation, this can obscure why a document is flagged or an equivalency suggested, complicating accountability and the detection of hidden biases. Opacity underscores the need for explainable systems and human review.
Contextual Understanding
Credential evaluation often requires nuanced interpretation of institutional practices, grading scales, or national education systems. AI can extract and structure data points, but it cannot easily determine, for instance, whether a three-year degree from one system equates to a four-year degree elsewhere. Over-reliance on AI in such cases risks oversimplification and inaccurate equivalency judgments.
Overconfidence and “Automation Bias”
One subtle but significant risk is automation bias—the human tendency to over-trust machine outputs, even when they are flawed. If teams treat AI-generated equivalencies authoritative rather than provisional, errors may propagate through the evaluation process unchecked. This risk is magnified when systems present outputs without transparent confidence levels or without explanations for how conclusions were reached.
Bias and Representation
AI models are trained on data, and if the training data does not adequately reflect global diversity in qualifications, institutional formats, or languages, the system may underperform on certain regions or groups. This creates a risk of structural bias, where applicants from underrepresented systems face disproportionately higher error rates or are flagged as anomalies simply because their documentation differs from the training norm.
Fraud and Adversarial Manipulation
AI can be a tool against fraud, but it can also be manipulated. Synthetic document generation and adversarial modifications (e.g., injections to instruct LLMs) present new challenges. Institutions that rely too heavily on automation risk falling into a cycle of escalation between fraudsters and fraud detection algorithms.
Cost-Benefit Misalignment
While AI promises efficiency, implementation is not without cost—both financial and organizational. Institutions need to balance the investment in AI infrastructure, training, and oversight against the actual gains in efficiency. In some contexts, partial automation or hybrid systems may yield more practical results than attempting to automate end-to-end evaluation.
Addressing the Concerns – Responsible AI Use
The limitations outlined above do not argue against the adoption of AI in credential evaluation; rather, they emphasize the need for responsible integration. Institutions that view AI as a partner, rather than a replacement, are better positioned to reap efficiency gains while safeguarding fairness, accuracy, and trust. Several design principles can help achieve this balance:
Human-in-the-Loop Design
The most sustainable model is not one where AI replaces evaluators, but where it augments them. Routine, repetitive tasks—such as extracting fields from transcripts or classifying document types—are well-suited for AI. Evaluators, in turn, focus on the nuanced judgments that require contextual expertise. This division of labor ensures that AI accelerates processes without displacing the professional discernment at the core of recognition work. This has been shown to be an effective design in critical decision making fields - curbing algorithmic bias, promoting fairness, and delivering better outcomes
Flagging Low-Confidence Cases
A central strength of AI is its ability to quantify uncertainty. For example, if an OCR system processes a low-quality scan and assigns a low confidence score to a grade field, the system can automatically flag the case for human review. In practice, this ensures that evaluators focus their attention precisely where ambiguity is highest, while routine cases flow through more quickly. AI becomes a triage system, not just an automation tool.
AI as a “Second Pair of Eyes”
Beyond triaging, AI can serve as a second layer of scrutiny. By surfacing inconsistencies, anomalies, or unusual equivalency mappings, AI highlights issues a human might overlook under time constraints. For example, if a degree title appears mismatched with the issuing institution’s known programs, the system can raise an alert. Far from replacing evaluators, this strengthens their ability to detect outliers and potential fraud.
Explainable AI and Retrieval-Augmented Generation (RAG)
Transparency is critical in high-stakes evaluation. Evaluation guidelines and academic literature on use of AI in critical fields both emphasize the importance of explainble AI systems. Emerging approaches such as explainable AI and retrieval-augmented generation (RAG) allow systems to show which parts of a document, database, or accreditation reference informed a given decision. Rather than a black-box output—e.g., “Bachelor’s degree equivalent”—evaluators could see the supporting evidence (such as recognition lists, ministry data, or historical equivalency rulings). This improves trust and auditability.
Fine-Tuned Domain Models
General-purpose AI often falls short when applied to the specialized language of qualifications, grading systems, or institutional formats. Fine-tuning models on domain-specific corpora—such as credential evaluation guidelines, historical case files, or ministry datasets—can significantly improve performance. Such models are better able to interpret abbreviations, regional terms, and the subtle distinctions between qualification types.
Trust Frameworks and Verifiable Credentials
One of the most promising avenues lies outside AI itself: the rise of verifiable digital credentials. If documents are issued and shared within trust frameworks, authenticity can be established before evaluation even begins. AI tools can then operate on verified inputs, reducing the burden of fraud detection and increasing overall confidence in the system.
Continuous Auditing and Monitoring
Finally, AI systems cannot be static. Continuous auditing—through bias monitoring, accuracy testing, and fairness reviews—is essential. Just as evaluators periodically revisit their criteria, AI models must be re-tested against diverse datasets and real-world cases. Institutions that embed auditing into their workflows are more likely to catch drift, mitigate unintended bias, and maintain public trust.
Addressing the Concerns – Responsible AI Use
The limitations outlined above do not argue against the adoption of AI in credential evaluation; rather, they emphasize the need for responsible integration. Institutions that view AI as a partner, rather than a replacement, are better positioned to reap efficiency gains while safeguarding fairness, accuracy, and trust. Several design principles can help achieve this balance:
Human-in-the-Loop Design
The most sustainable model is not one where AI replaces evaluators, but where it augments them. Routine, repetitive tasks—such as extracting fields from transcripts or classifying document types—are well-suited for AI. Evaluators, in turn, focus on the nuanced judgments that require contextual expertise. This division of labor ensures that AI accelerates processes without displacing the professional discernment at the core of recognition work. This has been shown to be an effective design in critical decision making fields - curbing algorithmic bias, promoting fairness, and delivering better outcomes
Flagging Low-Confidence Cases
A central strength of AI is its ability to quantify uncertainty. For example, if an OCR system processes a low-quality scan and assigns a low confidence score to a grade field, the system can automatically flag the case for human review. In practice, this ensures that evaluators focus their attention precisely where ambiguity is highest, while routine cases flow through more quickly. AI becomes a triage system, not just an automation tool.
AI as a “Second Pair of Eyes”
Beyond triaging, AI can serve as a second layer of scrutiny. By surfacing inconsistencies, anomalies, or unusual equivalency mappings, AI highlights issues a human might overlook under time constraints. For example, if a degree title appears mismatched with the issuing institution’s known programs, the system can raise an alert. Far from replacing evaluators, this strengthens their ability to detect outliers and potential fraud.
Explainable AI and Retrieval-Augmented Generation (RAG)
Transparency is critical in high-stakes evaluation. Evaluation guidelines and academic literature on use of AI in critical fields both emphasize the importance of explainble AI systems. Emerging approaches such as explainable AI and retrieval-augmented generation (RAG) allow systems to show which parts of a document, database, or accreditation reference informed a given decision. Rather than a black-box output—e.g., “Bachelor’s degree equivalent”—evaluators could see the supporting evidence (such as recognition lists, ministry data, or historical equivalency rulings). This improves trust and auditability.
Fine-Tuned Domain Models
General-purpose AI often falls short when applied to the specialized language of qualifications, grading systems, or institutional formats. Fine-tuning models on domain-specific corpora—such as credential evaluation guidelines, historical case files, or ministry datasets—can significantly improve performance. Such models are better able to interpret abbreviations, regional terms, and the subtle distinctions between qualification types.
Trust Frameworks and Verifiable Credentials
One of the most promising avenues lies outside AI itself: the rise of verifiable digital credentials. If documents are issued and shared within trust frameworks, authenticity can be established before evaluation even begins. AI tools can then operate on verified inputs, reducing the burden of fraud detection and increasing overall confidence in the system.
Continuous Auditing and Monitoring
Finally, AI systems cannot be static. Continuous auditing—through bias monitoring, accuracy testing, and fairness reviews—is essential. Just as evaluators periodically revisit their criteria, AI models must be re-tested against diverse datasets and real-world cases. Institutions that embed auditing into their workflows are more likely to catch drift, mitigate unintended bias, and maintain public trust.
Looking Ahead – The Future of AI in Evaluation
The trajectory of AI in credential evaluation is not about a sudden transformation but about incremental, layered adoption. As technologies mature, several developments are likely to shape the field:
From Automation to Decision Support
The emphasis will shift from “automating tasks” to building decision-support ecosystems. In this model, AI tools serve as advisors: surfacing anomalies, suggesting equivalencies, and contextualizing applicants’ profiles—always subject to human validation.
Integration with Verifiable Digital Credentials
If verifiable digital credential ecosystems mature, AI’s role could evolve from detecting fraud to optimizing equivalency and recognition. Verified authenticity at the source frees AI (and evaluators) to focus on interpretation rather than verification.
Greater Interoperability with Global Databases
AI systems will increasingly connect with international recognition databases, accreditation registries, and qualification frameworks. This interoperability can reduce duplication of effort, ensure consistency, and help evaluators access authoritative data in real time.
Standardization through Policy and Governance
As adoption grows, the role of policymakers and professional bodies will become central. Standards for explainability, bias monitoring, and human oversight will help align institutional practices with ethical and legal expectations. This may lead to sector-wide guidelines or accreditation standards for AI-assisted evaluation.
Looking Ahead – The Future of AI in Evaluation
The trajectory of AI in credential evaluation is not about a sudden transformation but about incremental, layered adoption. As technologies mature, several developments are likely to shape the field:
From Automation to Decision Support
The emphasis will shift from “automating tasks” to building decision-support ecosystems. In this model, AI tools serve as advisors: surfacing anomalies, suggesting equivalencies, and contextualizing applicants’ profiles—always subject to human validation.
Integration with Verifiable Digital Credentials
If verifiable digital credential ecosystems mature, AI’s role could evolve from detecting fraud to optimizing equivalency and recognition. Verified authenticity at the source frees AI (and evaluators) to focus on interpretation rather than verification.
Greater Interoperability with Global Databases
AI systems will increasingly connect with international recognition databases, accreditation registries, and qualification frameworks. This interoperability can reduce duplication of effort, ensure consistency, and help evaluators access authoritative data in real time.
Standardization through Policy and Governance
As adoption grows, the role of policymakers and professional bodies will become central. Standards for explainability, bias monitoring, and human oversight will help align institutional practices with ethical and legal expectations. This may lead to sector-wide guidelines or accreditation standards for AI-assisted evaluation.
Conclusion
The debate around AI in credential evaluation often falls into extremes: either overstating its potential as a universal solution or understating its relevance to a narrow set of repetitive tasks. In reality, AI’s role lies somewhere between these poles. It is neither a panacea nor a threat, but a set of tools that, when responsibly integrated, can meaningfully improve the efficiency, accuracy, and fairness of evaluation processes.
By automating routine steps, flagging ambiguities, and providing a second layer of scrutiny, AI allows human experts to devote more time to complex cases and nuanced judgments. At the same time, embedding safeguards—human-in-the-loop oversight, explainability, domain fine-tuning, and continuous auditing—ensures that efficiency does not come at the expense of trust.
Credential evaluation is ultimately about fairness, transparency, and recognition of human achievement across borders. AI, properly integrated, can strengthen that mission. The task ahead is not to decide whether AI belongs in evaluation, but to define how it should be designed, governed, and used to uphold the integrity of the field.
References
ACEI. (2023). AI in international credential evaluation: Promise and pitfalls. Association of International Credential Evaluators. Retrieved from https://acei-global.org/ai-in-international-credential-evaluation-promise-and-pitfalls
AICE. (2024). Use of Artificial Intelligence in Credential Evaluation. Association of International Credential Evaluators Report. Retrieved from https://aice-eval.org/wp-content/uploads/2025/03/Use-of-Artificial-Intelligence-in-Credential-Evaluation-Nov-2024-Report-AICE-1.pdf
Bozkurt, A. (2025). Trust, credibility, and transparency in human–AI interaction. ResearchGate. Retrieved from https://www.researchgate.net/profile/Aras-Bozkurt/publication/387737803_Trust_Credibility_and_Transparency_in_Human-AI_Interaction
CIMEA. (n.d.). Artificial Intelligence and Recognition of Qualifications. CIMEA. Retrieved from https://www.cimea.it/Upload/Documenti/Artificial_Intelligence_and_Recognition_of_Qualifications.pdf
Cearley, S. L., Krug, K., & Morrison, A. S. (n.d.). Introduction to Research in International Education (Part 2): Building a Research Roadmap for Credential Evaluation. Scholaro / AACRAO. Retrieved from https://cdn.scholaro.com/pdf/AACRAO-Intro-to-Research-in-International-Ed-Part-2.pdf
Dzindolet, M., et al. (2025). Automation bias in human–AI collaboration. AI & Society. Springer. Retrieved from https://link.springer.com/article/10.1007/s00146-025-02422-7
European Journal of Computer Science and Information Technology (EJCSIT). (2025). Humans-in-the-Loop in high-risk AI decision-making. Retrieved from https://eajournals.org/ejcsit/wp-content/uploads/sites/21/2025/07/Humans-in-the-Loop.pdf
EJCSIT. (2025). The evolving role of human-in-the-loop evaluations in advanced AI systems. Retrieved from https://eajournals.org/ejcsit/vol13-issue-9-2025/the-evolving-role-of-human-in-the-loop-evaluations-in-advanced-ai-systems
IJCRT. (2025). AI & OCR-enabled document verification. International Journal of Creative Research Thoughts. Retrieved from https://ijcrt.org/papers/IJCRT25A5469.pdf
Wang, Y., et al. (2024). Human-centered explainable AI: Aligning usability and transparency. Frontiers in Artificial Intelligence. Retrieved from https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1456486/full
Conclusion
The debate around AI in credential evaluation often falls into extremes: either overstating its potential as a universal solution or understating its relevance to a narrow set of repetitive tasks. In reality, AI’s role lies somewhere between these poles. It is neither a panacea nor a threat, but a set of tools that, when responsibly integrated, can meaningfully improve the efficiency, accuracy, and fairness of evaluation processes.
By automating routine steps, flagging ambiguities, and providing a second layer of scrutiny, AI allows human experts to devote more time to complex cases and nuanced judgments. At the same time, embedding safeguards—human-in-the-loop oversight, explainability, domain fine-tuning, and continuous auditing—ensures that efficiency does not come at the expense of trust.
Credential evaluation is ultimately about fairness, transparency, and recognition of human achievement across borders. AI, properly integrated, can strengthen that mission. The task ahead is not to decide whether AI belongs in evaluation, but to define how it should be designed, governed, and used to uphold the integrity of the field.
References
ACEI. (2023). AI in international credential evaluation: Promise and pitfalls. Association of International Credential Evaluators. Retrieved from https://acei-global.org/ai-in-international-credential-evaluation-promise-and-pitfalls
AICE. (2024). Use of Artificial Intelligence in Credential Evaluation. Association of International Credential Evaluators Report. Retrieved from https://aice-eval.org/wp-content/uploads/2025/03/Use-of-Artificial-Intelligence-in-Credential-Evaluation-Nov-2024-Report-AICE-1.pdf
Bozkurt, A. (2025). Trust, credibility, and transparency in human–AI interaction. ResearchGate. Retrieved from https://www.researchgate.net/profile/Aras-Bozkurt/publication/387737803_Trust_Credibility_and_Transparency_in_Human-AI_Interaction
CIMEA. (n.d.). Artificial Intelligence and Recognition of Qualifications. CIMEA. Retrieved from https://www.cimea.it/Upload/Documenti/Artificial_Intelligence_and_Recognition_of_Qualifications.pdf
Cearley, S. L., Krug, K., & Morrison, A. S. (n.d.). Introduction to Research in International Education (Part 2): Building a Research Roadmap for Credential Evaluation. Scholaro / AACRAO. Retrieved from https://cdn.scholaro.com/pdf/AACRAO-Intro-to-Research-in-International-Ed-Part-2.pdf
Dzindolet, M., et al. (2025). Automation bias in human–AI collaboration. AI & Society. Springer. Retrieved from https://link.springer.com/article/10.1007/s00146-025-02422-7
European Journal of Computer Science and Information Technology (EJCSIT). (2025). Humans-in-the-Loop in high-risk AI decision-making. Retrieved from https://eajournals.org/ejcsit/wp-content/uploads/sites/21/2025/07/Humans-in-the-Loop.pdf
EJCSIT. (2025). The evolving role of human-in-the-loop evaluations in advanced AI systems. Retrieved from https://eajournals.org/ejcsit/vol13-issue-9-2025/the-evolving-role-of-human-in-the-loop-evaluations-in-advanced-ai-systems
IJCRT. (2025). AI & OCR-enabled document verification. International Journal of Creative Research Thoughts. Retrieved from https://ijcrt.org/papers/IJCRT25A5469.pdf
Wang, Y., et al. (2024). Human-centered explainable AI: Aligning usability and transparency. Frontiers in Artificial Intelligence. Retrieved from https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1456486/full