When AI Does the Thinking: The Hidden Costs for Software Engineering Research

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Author: Denis Avetisyan

A thought experiment explores how over-dependence on generative AI tools could subtly undermine core technical competencies and erode the foundations of trustworthy research in software engineering.

This review uses design fiction to examine the potential for a ‘competence crisis’ driven by the uncritical adoption of AI-assisted methods and advocates for a return to first principles thinking and value-sensitive design.

The increasing efficiency promised by generative AI in software engineering research belies a potential erosion of fundamental expertise. This tension is explored in ‘The Competence Crisis: A Design Fiction on AI-Assisted Research in Software Engineering’, which employs a speculative near-future scenario to illuminate how over-reliance on automated tools could diminish core competencies and impact research integrity. The paper demonstrates that uncritical adoption risks not only hindering domain knowledge but also complicating verification processes and effective mentorship. Will the software engineering research community proactively redefine proficiency and responsibility to navigate this evolving landscape, or will automated assistance subtly reshape the foundations of scholarly practice?

The Erosion of Trust: Peer Review in an Age of Complexity

A growing apprehension within the software engineering community centers on the efficacy of peer review, indicating potential systemic weaknesses in how research is validated. This isn’t simply about isolated incidents of flawed studies slipping through; rather, a broader concern is emerging that the current methodologies struggle to keep pace with the field’s accelerating innovation. The increasing sophistication of software architectures, particularly with the integration of artificial intelligence, demands a level of specialized knowledge that may not be consistently present among reviewers. Consequently, critical errors or incomplete analyses risk being overlooked, potentially impacting the broader adoption of flawed techniques and hindering genuine progress. This challenge isn’t a reflection of reviewer competence, but a signal that the foundational processes for evaluating research require substantial re-evaluation and adaptation to maintain the integrity of the field.

The escalating complexity of modern software systems, coupled with the relentless acceleration of technological advancement, is fundamentally challenging the efficacy of traditional peer review. Existing methodologies, largely unchanged for decades, struggle to keep pace with innovations in areas like artificial intelligence, distributed systems, and cloud computing. Reviewers, even those with substantial experience, may lack the specialized knowledge required to fully grasp the nuances of cutting-edge research, leading to potentially flawed evaluations. This isn’t simply a matter of increased workload; it’s a systemic issue where the very foundations of the review process are being strained by the sheer velocity and intricacy of contemporary software development, raising concerns about the reliability of published research and its impact on the field.

Emerging data from the 2026 International Conference on Software Engineering (ICSE) pre-survey reveals a growing dissatisfaction with the efficacy of current peer review processes. Respondents increasingly express concern that flawed or incomplete research is slipping through evaluation, a trend coinciding with a projected surge in submissions. Forecasts estimate 1469 submissions for the 2026 conference, nearly double the 797 received in 2023. This escalating volume, coupled with rising anxieties about review quality, suggests a potential crisis in the field’s ability to reliably validate research findings and maintain the integrity of the software engineering knowledge base.

The escalating complexity of modern software and the proliferation of AI-driven systems are fundamentally challenging the efficacy of traditional peer review. Assessing the validity of these intricate creations demands a level of specialized technical expertise that is becoming increasingly scarce among reviewers, creating a critical bottleneck in the research validation process. Simply confirming the presence of results is insufficient; reviewers must now possess the capacity to deeply understand the underlying algorithms, data structures, and potential failure modes inherent in these systems – a task requiring significant time investment and specialized knowledge. This shift necessitates a re-evaluation of current review practices, potentially incorporating more specialized reviewers, enhanced review guidelines focusing on technical depth, or even automated tools designed to assist in the identification of subtle but critical flaws.

The Peril of Superficial Mastery

The increasing prevalence of generative AI tools in software development introduces a risk to the cultivation of fundamental technical skills among engineers. While these tools can automate code generation and problem-solving, over-reliance may discourage developers from acquiring in-depth knowledge of algorithms, data structures, and system design principles. This is particularly concerning for junior engineers whose foundational learning may be supplanted by AI-assisted solutions, potentially limiting their ability to independently analyze, debug, and optimize code, or to innovate beyond the capabilities of the AI. The resultant skill gap could impede long-term advancements in the field and create dependence on proprietary AI systems.

Increased dependence on AI-generated code and solutions risks cultivating a “Dash-shaped profile” among software engineers, characterized by broad but shallow technical competence. This profile manifests as familiarity with a wide range of tools and technologies, coupled with a lack of in-depth understanding of underlying principles such as data structures, algorithms, or system architecture. Consequently, individuals with this profile may be able to integrate AI-generated components into projects, but lack the capacity to rigorously audit the code for errors, security vulnerabilities, or performance bottlenecks. The inability to critically evaluate results stems from a deficient base of foundational knowledge, hindering effective debugging, optimization, and independent problem-solving beyond the scope of the AI’s output.

A T-shaped profile, denoting broad general knowledge coupled with deep expertise in a specific area, is increasingly vital for modern software engineering. This profile enables professionals to integrate and understand diverse technologies – the ‘broad’ aspect – while possessing the specialized skills necessary for critical evaluation, debugging, and innovative problem-solving. Effective verification of complex systems, particularly those incorporating AI-generated components, demands this depth of understanding; superficial knowledge is insufficient to identify subtle errors or security vulnerabilities. The ability to connect high-level system requirements to low-level implementation details, a hallmark of the T-shaped professional, is therefore essential for maintaining software quality and ensuring system reliability.

Insufficient technical expertise among researchers and developers creates a significant risk when integrating AI-generated code or data into the research lifecycle. Effective auditing and testing of AI outputs require a deep understanding of underlying algorithms, data structures, and potential failure modes. Without this foundational knowledge, it becomes difficult to identify inaccuracies, biases, or security vulnerabilities present in the AI’s output. This lack of critical evaluation can lead to the propagation of errors, compromised research integrity, and ultimately, unreliable results. Consequently, organizations must prioritize maintaining and developing strong technical skills alongside the adoption of AI tools to mitigate these risks and ensure the validity of research outcomes.

The Fallacy of Algorithmic Transposition

Bio-Photonic Interfaces represent a highly interdisciplinary field demanding expertise across biology, photonics, and engineering. Our design fiction scenario utilized this complexity as a foundational element, positing interfaces that directly couple biological systems with photonic components for data transfer and control. This necessitates a deep understanding of biological signaling pathways, optical principles – including light-matter interactions and waveguide design – and the engineering challenges of biocompatibility and signal processing. The intentional creation of this complex domain allowed us to explore potential failure modes arising from misapplication of established, but fundamentally incompatible, computational concepts to biological systems.

The scenario details a misapplication of the Paxos algorithm, a consensus protocol originating in distributed computing, to a biological system within a Bio-Photonic Interface. Paxos is designed to ensure agreement on a single value in a distributed system, even in the presence of failures; however, its core assumptions regarding discrete states, synchronous communication, and clearly defined failure modes do not translate directly to the continuous, asynchronous, and inherently stochastic nature of biological processes. Researchers, relying on AI-generated solutions, failed to recognize this incompatibility, treating cellular signaling pathways as equivalent to computer network nodes and attempting to impose a computational logic onto a fundamentally different system. This resulted in unstable feedback loops and ultimately, system failure, demonstrating that algorithms developed for one domain cannot be reliably ported to another without rigorous adaptation and validation.

The critical failure of the Bio-Photonic Interface resulted directly from the misapplication of the Paxos algorithm, a consensus protocol designed for distributed computing systems. This occurred because the researchers lacked sufficient understanding of both biological systems and the limitations of translating computer science principles to biological contexts. Specifically, the algorithm’s assumptions regarding discrete states and synchronous communication were incompatible with the continuous and asynchronous nature of biological processes. This incompatibility led to a cascading failure within the interface, demonstrating that algorithmic solutions, however robust in their native domain, are ineffective – and potentially dangerous – when implemented without a comprehensive understanding of the underlying system’s fundamental principles.

The potential for catastrophic failure in complex systems, such as Bio-Photonic Interfaces, is significantly increased by a reliance on algorithmic solutions without thorough validation grounded in foundational principles. Even seemingly minor errors in implementation or application – such as incorrectly adapting a consensus algorithm designed for distributed computing to a biological context – can propagate through the system and lead to critical malfunctions. Rigorous verification processes, informed by a deep understanding of first-principles – the fundamental laws and properties governing the system – are therefore essential to identify and mitigate these risks before deployment. This necessitates a holistic approach that combines computational modeling with empirical testing and expert knowledge, rather than solely relying on AI-generated outputs or automated solutions.

Reclaiming Intellectual Sovereignty

In an era increasingly shaped by generative AI, research practice demands a renewed emphasis on resilience – the capacity to independently confront and resolve challenges without habitual dependence on automated tools. This isn’t simply about avoiding technological crutches, but cultivating a robust skillset for problem-solving that transcends specific software or algorithms. A researcher’s ability to critically analyze information, formulate hypotheses, and design experiments remains fundamental, ensuring investigations aren’t merely guided by AI outputs but driven by genuine intellectual curiosity and rigorous methodology. The true value lies in maintaining the capacity to ‘work through’ complexities, even when efficient AI solutions are available, as this independent thinking safeguards against potential biases, errors, and a diminishing of core research competencies. Ultimately, resilient researchers are equipped to not only utilize AI effectively but also to question, validate, and improve upon its contributions.

The increasing prevalence of AI-generated content necessitates a robust skillset beyond simply accepting outputs at face value; this is where verification literacy becomes paramount. Researchers must cultivate the ability to rigorously audit and test information produced by artificial intelligence, moving past superficial comprehension to assess underlying logic, potential biases, and factual accuracy. This isn’t merely about identifying errors, but proactively dissecting the reasoning behind an AI’s conclusions – essentially, treating the AI as a ‘black box’ demanding thorough inspection. Without this critical evaluation, there’s a significant risk of perpetuating misinformation, accepting flawed analyses, and ultimately eroding the integrity of research itself. Cultivating verification literacy, therefore, isn’t an optional addendum to research practices, but a foundational requirement for navigating an increasingly AI-driven landscape.

Researchers increasingly recognize the necessity of integrating Value-Sensitive Design principles throughout the entire research lifecycle when working with artificial intelligence. This proactive approach moves beyond simply assessing AI outputs for accuracy; it demands a deliberate consideration of how these technologies shape – and potentially erode – human expertise and critical thinking skills. By explicitly accounting for these impacts, researchers can design AI systems and workflows that augment, rather than replace, essential cognitive abilities. This includes prioritizing transparency in AI decision-making processes, fostering user understanding of algorithmic limitations, and actively mitigating biases that could reinforce existing inequalities or stifle intellectual curiosity. Ultimately, Value-Sensitive Design ensures that the pursuit of innovation with AI doesn’t inadvertently diminish the very qualities – rigorous analysis, independent thought, and nuanced judgment – that drive scientific progress.

A robust and ethical research future hinges on proactively equipping scholars with the core skills to navigate the evolving landscape of artificial intelligence. Investment in educational programs that prioritize foundational principles – statistical reasoning, methodological rigor, and domain expertise – is not merely beneficial, but essential. Such training moves beyond tool-specific instruction to cultivate critical reasoning abilities, empowering researchers to independently assess the validity and reliability of AI-generated outputs. By emphasizing understanding over automation, these initiatives foster a research community capable of responsibly leveraging AI’s potential while safeguarding against the uncritical acceptance of potentially flawed or biased information. Ultimately, this approach ensures that human expertise remains central to the scientific process, driving innovation with both intelligence and integrity.

The exploration of AI’s role in software engineering research, as detailed in the article, reveals a subtle but critical danger: the potential for diminished fundamental competence. This echoes Blaise Pascal’s observation, “The eloquence of the tongue does not convince the mind, but it captivates the senses.” Just as persuasive rhetoric can mask a lack of substance, generative AI, while offering apparent efficiency, risks obscuring a decline in ‘first principles thinking’ and genuine technical understanding. The article rightly cautions that uncritical acceptance of AI-generated results could prioritize expediency over rigorous verification, ultimately jeopardizing the trustworthiness of the research itself. It is a potent reminder that true progress demands not simply doing things quickly, but understanding why they work.

What’s Next?

The presented scenario, deliberately constructed as a cautionary tale, exposes a simple truth: competence is not a static attribute, but a continuously cultivated practice. The field now faces a meta-problem. The increasing accessibility of generative AI tools demands not merely an assessment of their utility, but a rigorous examination of their impact on the process of learning and discovery within software engineering research. The question is not whether these tools are powerful-that is self-evident-but what is lost when the foundational skills required to validate their outputs atrophy.

Future work should abandon the pursuit of ever-more-complex AI solutions and instead focus on distilling first principles. The current emphasis on demonstrable output obscures a critical need for demonstrable understanding. The field must devise metrics not for the speed of innovation, but for the depth of comprehension. A reductionist approach-identifying the minimal skillset required for trustworthy research-will prove more valuable than any algorithmic refinement.

Ultimately, the true challenge lies in resisting the seductive allure of effortless results. The temptation to offload cognitive burden onto AI is strong, but this transfer carries a cost. The research community must consciously prioritize the cultivation of fundamental technical competence, not as an end in itself, but as the necessary condition for meaningful progress. Simplicity, in this context, is not limitation-it is intelligence.

Original article: https://arxiv.org/pdf/2601.19628.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-28 12:36