Sumber ilustrasi: Magnific
28 Mei 2026 09.54 WIB – Akar
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Desanomia [28.05.2026] Recently, cases have increasingly emerged in which individuals—students, pupils, or intellectuals—are found using AI to produce academic or scientific work. In such cases, the creator is often considered dishonest or accused of violating ethical standards. Educational institutions respond by imposing sanctions, sometimes even permanent ones, by marking the individual or placing them on a blacklist.
But can the path taken by educational institutions truly be considered the correct one? Or should these cases instead become an opportunity for deeper reflection? In this sense, the use of AI in academic or scientific work, along with the emergence of AI detectors, can be understood as an entry point for reflecting on the reality of the modern educational evaluation system. The problem is not just that individuals cheat, plagiarize, or manipulate academic work. The more fundamental problem is that educational evaluation itself is becoming increasingly incapable of distinguishing between work that emerges from genuine thought and work generated by intelligent machines.
In a healthy educational system, evaluation should function as a way to recognize the development of the subject: how students understand problems, construct arguments, test ideas, correct mistakes, and form intellectual maturity. Assessment should not focus solely on final results, but on the process of intellectual growth itself. Yet in modern practice, evaluation has gradually shifted into the inspection of administrative outputs: grades, papers, publications, citations, indexes, certificates, and conformity to formal standards. Education no longer primarily evaluates the process of becoming, but rather outcomes that can be administratively verified.
At this point, AI sharply exposes the weakness of the system. Machines are now capable of producing texts that appear academic, complete with structure, style, argumentation, and even formal citations. If the evaluation system only examines external form, then AI-generated products can easily appear as legitimate intellectual work. AI not only create a new crisis; it reveals that educational evaluation had already become overly dependent on symbolic surfaces long before AI arrived.
The emergence of AI detectors further clarifies this condition. These tools are understood as technical solutions for distinguishing human work from machine-generated work. Yet more fundamentally, the existence of AI detectors constitutes an implicit admission that the natural capacity embedded within pedagogical relationships has weakened. Teachers and lecturers are no longer sufficiently able to recognize whether a work genuinely arises from the intellectual development of the student or just from algorithmic reproduction.
In a living educational relationship, however, a work is never just a dead text. It is the trace of a developing subject. A teacher who genuinely follows a student’s intellectual process usually recognizes the rhythm of the student’s reasoning, the way problems are formulated, characteristic patterns of error, shifts in understanding, and the way ideas are connected. Under such conditions, the question “Was this written by AI or not?” does not become the central issue, because the birth of the work itself is already understood through an ongoing pedagogical relationship.
Dependence on AI detectors demonstrates that this relationship has gradually shifted into a form of mass administrative management. Teachers and lecturers no longer read intellectual development; they inspect final products. When the final product becomes questionable, one machine is called upon to verify whether another machine participated in producing it. The paradox is unmistakable: education has begun using machines to determine whether human beings are still thinking as human beings.
For this reason, AI detectors are not a mere technical tool. They are epistemological symptoms. Their existence indicates that the evaluation system no longer fully trusts human judgment. Validation gradually moves away from pedagogical relationships and toward algorithmic verification. Education loses the closeness between mentor and learner and replaces it with statistical and probabilistic surveillance.
When these systemic weaknesses become visible to the public, the burden of failure is usually shifted onto students. They are positioned as deviant actors, while the system itself continues to preserve its authority as the legitimate standard. Punishment is directed toward individuals, yet the deeper questions are avoided: why is the educational system incapable of recognizing authentic learning processes? Why does education only discover violations after the final product appears?
It is here that the mechanism of institutional scapegoating operates. Students caught cheating become evidence that the system still protects integrity. Yet the situation can also be interpreted in the opposite way: the system is only capable of punishing symptoms after simulation has already taken place. The severity of sanctions does not necessarily demonstrate educational credibility. It may instead function as a means of preserving institutional legitimacy without addressing the roots of the problem.
For us, these cases should become a moment for reflection and for directing critical questions toward institutions themselves: by what means does society actually know that the educational evaluation system is credible? Public trust is generally placed in signs of legitimacy such as accreditation, rankings, degrees, certificates, indexed journals, and institutional reputation. Yet none of these are direct proof that meaningful educational processes are truly taking place. They only indicate that the system possesses formal mechanisms of validation.
The crisis of academic integrity is not merely a moral crisis of individuals, but an epistemological crisis within education itself. The system claims to evaluate knowledge, yet in reality it often evaluates only representations of knowledge. The system claims to produce new human beings, while in practice it only produces documents of success. The system claims to protect originality, yet it no longer possesses pedagogical relationships strong enough to recognize the birth of genuine thought.
What, then, is the way forward? The solution cannot simply consist of stricter rules, wider use of AI detectors, or harsher punishments. What is required is a reorientation of education itself: from product-based evaluation toward process-based evaluation; from administrative surveillance toward pedagogical relationships; from inspection of formal structures toward recognition of intellectual development. Without such changes, education will continue punishing symptoms while preserving the underlying disease.
From all of this, it can be said that both AI-generated works and AI detectors reveal the same reality: modern education may have long operated without genuinely carrying out education itself. It is capable of producing grades, degrees, publications, and reputations without ensuring that subjects truly undergo intellectual development. At this point, its deepest failure becomes visible: not because machines entered the educational sphere, but because the educational sphere itself has gradually transformed into a machine of simulation.
What do you think? (njd)
Note: This article was made as part of a dedicated effort to bring everyday life around us to our minds.
