Artificial Intelligence and Educational Value Creation: Does Computational Rationality Complement Bounded Rationality in Asian Learning Environments?
Shahreer Zahan explores how attention to how the blending of computational and bounded rationality can amplify value creation for students and communities.
A promising theoretical question in the application of AI to Asian education is whether computational rationality can complement humans' natural bounded rationality in learning and decision-making? This is a particularly relevant question within Asian educational contexts where traditional pedagogy meets advanced technology development, generating its own tensions and possibilities for value creation.
The idea of bounded rationality, which posits that human beings lack infinite time and computational capabilities to make decisions, has radically changed the understanding of cognitive psychology in decision-making (Simon, 1955). Instead of optimizing, humans "satisfice" and search for good enough solutions rather than the best ones (Simon, 1979). This framework has proven remarkably durable in explaining educational decision-making, from student choices about university attendance to teachers' pedagogical strategies (Simon, 1991). Asian education systems, known for their systematic curricula and examination-oriented environment, are representative of such settings where bound rationality influences learning outcomes based on pre-existing heuristics and decision-making processes.
Computational rationality, by contrast, represents a paradigm where intelligent systems identify decisions with the highest expected utility while accounting for computational costs in complex, real-world problems (Lewis et al., 2014). Recent integration between artificial intelligence, cognitive science, and neuroscience has proposed computational rationality as a potential means of compensating for human cognitive boundedness (Lieder & Griffiths, 2019). And this is the challenge for Asian educators: How can the power of computing in AI systems complement, rather than interfere with, what is referred to as adaptive and context-sensitive decision-making that characterizes effective teaching and learning?
There is cause for cautious optimism based on the empirical evidence from Asian settings. Studies on the implementation of AI in K–12 (publicly funded primary & secondary schooling) education in Asia show that how successful the integration would be is based largely on infrastructure preparedness, teacher training and cultural alignment (Weng et al., 2024). Where you have countries such as China and Singapore with advanced AI education policies, you also have others that can’t provide basic educational infrastructures. This disparity highlights a critical insight: computational rationality cannot substitute for the contextual understanding and cultural sensitivity inherent in bounded rationality; it must complement them.
Value creation pedagogy provides a useful lens for understanding this complementarity (Wiley & Hilton, 2018). When students create value for external stakeholders through their educational learning activities, they actively engage in authentic problem-solving that demands both computational support & human judgment. AI systems can process vast datasets, identify patterns, and generate personalized learning pathways of computational tasks that exceed human capacity (Russell & Norvig, 2021). However, the value judgment of what constitutes meaningful learning, as well as the ethical issues surrounding who gets to participate in engagement with stakeholders and whether culturally appropriate solutions remain firmly within the domain of bounded rationality, remains a concern.
This tension is especially evident in Asian educational contexts. Teacher surveys from the Asia and Pacific region, for instance, reveal that teachers do appreciate AI's potential in personalized learning and ease of school administration efficiency; they also express genuine concerns about over-reliance on technology, lack of cultural adaptation as well as potential erosion of critical human elements to teaching (UNESCO, 2025). On the other hand, teachers at mainstream educational history institutions have expressed reluctance to adopt AI technologies, which, however, does not result from an actual resistance to technology as such, but from a justified concern for disrupting established pedagogical relations and its cultural values (Chiu et al., 2023).
The theoretical implications are significant. Computational rationality and bounded rationality are predicated on fundamentally different assumptions about optimization, information processing, and decision timescales. AI is about optimization in a decision-making context, given defined parameters (utilizing probabilistic inference and learning). It is facilitator co-facilitators who cope in culturally loaded situations and who utilize tacit knowledge, emotional intelligence, and relational sensing, which animate the human actors doing thinking (Ahmed & Rahaman, 2025). It's not one or the other that's better; they just address two different areas of the problem we have with education.
AI-powered adaptive learning systems can identify knowledge gaps & suggest personalized content delivery as a knowledge assistant, a computational task requiring pattern recognition across millions of data points from the web. Teachers then exercise bounded rationality in interpreting these recommendations within their understanding of individual students' emotional states, family contexts, and cultural backgrounds. The AI provides the "what" through computational analysis; teachers provide the "how" and "why" through contextual judgment.
Asian educational environments also bring to the fore broader questions of equity, access and progression. Tools of generative AI might offer some hope in leveling the playing field here, making quality educational resources available to all, regardless of geographical and socioeconomic barriers (UNESCO, 2025). However, unlocking this potential would require a cooperative effort to address the digital divide, language diversity, and cultural differences, with attention to enhancing relevance constraints—challenges that can’t be resolved by computational rationality alone. With such a diverse learner population, a rational approach informed by cultural context becomes crucial for making AI tools effective for this demographic.
This blending of computational thinking and AI literacy in schools, as exemplified in Asian schools (Long & Magerko, 2020). By engaging in computational thinking, students acquire logically organized ways of solving problems that can be used to check or refine their intuitive thinking. And when these abilities are combined with an understanding of AI strengths and weaknesses, students can acquire what we might call upgraded bounded rationality—they are able to see where computational approaches provide value, yet also where human judgment remains essential.
Considering the prospects of AI, Asian educators must have to navigate several critical considerations. First, the focus of professional development should be on more than just technical AI literacy – it should also address critical AI literacy, i.e., understanding the social consequences and biases of AI, as well as when it is appropriate to use them (Nascimbeni & Burgos, 2016). Secondly, support structures within institutions must offer teachers time, resources and collaborative opportunities with AI integration while upholding pedagogical autonomy (Zawacki-Richter et al., 2019). Thirdly, policy frameworks should prioritize human centered design principles to establish conditions that support AI as a tool rather than its drivers (Cheng, Eric C. K, 2024).
Computational rationality and bounded rationality in Asian learning contexts are neither antagonistic nor hierarchical; instead, they are mutually enriching. AI systems excel at tasks such as simulating simulations, pattern matching in large datasets, or solving optimization problems within specific bounds. Human educators are skilled at tasks that require contextual judgment, emotional attunement, and ethical reasoning (Gervais, 2016). The educational value creation is not a function of substituting the former for the latter, but rather orchestrating their interactions in manners that respect both computational capability and human wisdom.
Asian education systems, as they have both a strong pedagogical background and rapid technology adoption, have a unique opportunity to lead the global way in this blending of worlds. Addressing these concerns calls for us to move past our cultural tendency towards technological solutionism and develop a nuanced account of both how computational and bounded rationality can amplify one another in the service of deeper learning, authentic engagement, and meaningful value creation for students and communities.
Shahreer Zahan is the Founder & CEO of Adroit Education, where he leads initiatives focused on student mentorship, academic development and global admissions strategy. He can be reached at- shahreerz@gmail.com. His work centers on building high-impact educational programs that prepare students for competitive international pathways through personalized guidance and research-driven support.
Photo by Martin de Arriba
