The transformation of value assessment driven by AI is approaching
The impact of Artificial Intelligence (AI) goes far beyond transforming our approaches to teaching and research, and it is not merely about replacing certain operational positions through improved efficiency. The core challenge lies in fundamentally changing the way we assess value. It can even be predicted that AI will help break the long-standing dominance of the “Five Onlys” — evaluation based solely on papers, titles, professional ranks, academic degrees, and awards — and, in doing so, foster the true spirit of educators.
Evaluation lies at the heart of management. Whether it is strategic planning, tactical execution, organizational design, personnel decisions, resource allocation, or the review of papers and projects, all forms of management are grounded in evaluation. The logic of human decision-making and behavioral change often follows the pattern: “You evaluate me first, and then I’ll decide what to do.” Evaluation acts as the guiding baton — once the evaluation criteria change, everything changes; without changing them, nothing truly changes. We allocate our time and energy according to how we are evaluated.
In the near future, blockchain-based methods, technologies, and systems will enable the continuous recording of each individual’s value contribution, rather than relying solely on discrete academic papers and publications as evidence for scholarly evaluation. One major advantage of digital technology is its ability to make management far more granular and increasingly timely—even real-time. The publication of an academic paper often takes months, a year, or even longer; by then, the value of new ideas or discoveries may have diminished in a rapidly changing society. In fact, many genuine insights in management do not emerge suddenly in lengthy academic works, but are gradually formed and exert influence through the ongoing interaction and iterative evolution between theory and practice.
In the rapidly evolving field of computer science, conference papers have long held greater importance than journal publications. Researchers typically submit their work to conferences, where their findings are promptly published online for others to reference and provide feedback. Regardless of whether a paper is accepted, its valuable contributions are protected by intellectual property rights and shared publicly. This process already reflects the logic of blockchain technology—though the granularity and timeliness of conference paper evaluation are still limited. Many insightful and talented individuals may not be inclined or skilled at writing full papers, yet they are willing to share a single sentence, a short reflection, or a brief idea — which may, in fact, contain the seeds of major scientific breakthroughs or social impact. Current systems of academic dissemination and recognition cannot effectively capture or reward such micro-level contributions, but AI can. It can both preserve and amplify these original insights rather than letting them fade into obscurity. At the same time, misleading claims, pseudoscience, and fake innovations will also be documented and traceable. The extensive data recording and storage enabled by AI and big data will help reduce academic misconduct and curb the emergence of scientific fraudsters, as the costs and risks of deception become far greater.
In the fast-evolving field of computer science, conference papers have long carried more weight than journal publications. Researchers typically submit their work to conferences, where their findings are promptly published online for others to reference and provide feedback. Whether or not a paper is accepted, any valuable contribution it contains is protected by intellectual property rights and made publicly accessible. This process essentially embodies the principles of blockchain technology—though the granularity and timeliness of conference paper evaluation remain limited. Many insightful and talented individuals may not be inclined or adept at writing full-length papers, yet they are willing to share a sentence, a brief reflection, or a short insight — which may well contain the seeds of significant scientific breakthroughs or profound social impact. Existing systems for publishing and recognizing academic achievements cannot effectively capture or reward such micro-level contributions, but AI can. It can ensure that these original insights are not overlooked and can greatly accelerate their development and influence. At the same time, seemingly serious nonsense, pseudoscience, and fake innovations will also be documented and traceable. The vast capacity for data dissemination and storage brought by AI and big data will significantly raise the costs of misconduct, thereby reducing academic fraud and curbing the emergence of scientific impostors.
AI will greatly promote the diversification and nonlinearity of evaluators, evaluation standards, evaluation purposes, and benefit feedback mechanisms, thereby fostering more motivating and effective innovation. Administrative evaluation tools such as “titles” and “academic ranks,” which are intertwined with resource allocation, will be significantly diluted as centralized evaluation power becomes more decentralized. With the support of big data, large models, and numerous generative AI applications, our fragmented insights and research efforts will be interconnected, generating a wide range of new possibilities. These possibilities will be referenced, refined, or utilized by various organizations and individuals — including enterprises, universities, hospitals, and many others we can or cannot yet imagine.
AI will greatly advance the diversification and nonlinearity of evaluators, evaluation criteria, purposes, and benefit feedback mechanisms, thereby fostering more motivating and effective innovation. Administrative evaluation instruments such as “titles” and “academic ranks,” which are closely tied to resource allocation, will see their influence significantly diminished as centralized evaluation power becomes increasingly decentralized. With the support of big data, large models, and a wide array of generative AI applications, our scattered insights and research efforts will be interconnected, giving rise to countless new possibilities. These possibilities will be referenced, refined, and applied by various organizations and individuals — including enterprises, universities, hospitals, and others we can or cannot yet envision.
In the AI-driven evaluation network, evaluators will include a wide range of real, potential, or occasional stakeholders and interested parties — no longer limited to the so-called “small peers,” “broad peers,” or “experts.” The emphasis on “peers” and “experts” itself inherently restricts the expression of creativity. The higher the tier of an academic journal, the stricter and more standardized its review process tends to be — yet these very norms and standards often exclude many truly innovative, especially original, ideas. Innovation is unconventional not only in its conclusions but also in its reasoning and logic. No enterprise has ever grown by merely imitating others’ cases or following statistical patterns. Ironically, today’s real-world managers are often distanced from those “profound” academic journals — they are neither reviewers nor readers. As AI continues to advance, the era in which the value, income, and even career prospects of individuals can be determined by a small number of paper reviewers (many of whom are doctoral students with little or no management experience) will eventually come to an end. In the future, practitioners who are genuinely engaged in management will play a more active role in evaluating value. Research and practice will become increasingly intertwined, the boundary between academia and real-world practice will gradually fade, and the era of “doing management research” in isolation, “enduring loneliness” in the ivory tower, will be over. In other words, in the field of management — a discipline rooted in practice — there is no such thing as “pure scholarship.”
I cannot predict what further changes AI will bring to the way teachers are evaluated, nor can I foresee how many years it will take for these transformations to become mainstream. Yet the impacts already visible are enough to alert us to our long-standing habits — the ways we conduct research, organize our work, allocate our efforts, and depend on existing evaluation systems. We must prepare ourselves in advance. AI will bring endless possibilities. In an unpredictable world, the only thing we can hold onto is our first principles. For the discipline of management practice, that first principle may well be the pursuit of truth — seeking genuine scholarship, addressing real contradictions, and contributing true value. The true judges of this “truth” are not limited to academics, but also include managers, entrepreneurs, leaders, students, teachers, and all those beyond academia who are committed to creating value. We need to shift our focus from the pathways and recognition of academic outputs to the creation of academic value and the realization of its impact.
The future is as near as our vision is far. Just as management must stay ahead of change, our approach to research and our anticipation of value evaluation must stay ahead of AI. We need to proactively and early step out of our—not-so-comfortable—comfort zones; otherwise, we may face a sudden and profound transformation at a time when we are no longer able to drive change ourselves.
