The power duo: unleashing cognitive potential through human-AI synergy in STEM and non-STEM education

AI-driven education tools are expected to impact over 2 billion learners worldwide in the coming years, transforming both STEM and non-STEM disciplines in unprecedented ways (Louly, 2024;Sandhu et al., 2024;World Economic Forum, 2024) Artificial Intelligence (AI) is revolutionizing education through personalized tutoring, real-time feedback, and adaptive learning experiences (Akavova et al., 2023). AI enables teachers to create individualized development plans according to the needs of the students. Its impact on intellectual tasks such as critical thinking, emotional intelligence, and moral reasoning is, however, a debatable topic (Çela et al., 2024). Greater dependence on AI-driven tools is a cause for concern with surface learning and minimal engagement with complex problem-solving and debates (Çela et al., 2024).While AI enhances education in all subjects, it does so unevenly between STEM and non-STEM fields, particularly in its engagement with structured logic-based learning versus interpretative, abstract reasoning (Nagaraj et al., 2023;Singer et al., 2023). Within STEM education, AI's analytical and structured logic nature provides excellent benefits in problem-solving, simulation, and automation of complex calculations. However, non-STEM fields, such as the humanities and social sciences, require more interpretative, ethical, and creative engagements that AI is less likely to be able to provide. This paper explores these differences while advocating for an even-keeled integration of AI that augments, rather than replaces, human teaching.Theoretical Perspectives on AI in Education: Learning theories offer a framework for understanding AI's application in education. In the context of Bloom's Taxonomy, AI can support lower-order thinking skills (knowledge, comprehension, application) in STEM education but lags in supporting higher-order skills like evaluation and synthesis (Essien et al., 2024). Within the context of Vygotsky's Zone of Proximal Development, for non-STEM, AI can be seen as a form of scaffold that can assist students with guided learning tasks but that still needs to be closely mediated by a human to facilitate the growth of abstract thought and creativity (Xue, 2023). Both theories emphasize the ability of AI to guide users in solvable problems however suggest the need for human intervention to achieve a deeper learning outcome.Intelligence in Education (AIEd) entails a broad range of varied tasks, ranging from adaptive learning systems to automated grading. AI, in STEM disciplines, is used to accelerate problemsolving and the grading process; for example, AI tools facilitate personalized tutoring to improve engagement and course performance in computational material, such as mathematics and engineering (Gupta et al., 2024;Mustafa, 2024). AI use in grading has reduced grading inconsistencies up to 44% compared to human grading (Gobrecht et al., 2024) which means less bias across STEM exams. Notwithstanding these prospects, however, there are apprehensions about bias and AI failure on tasks that entail subjective grading like grading for engineering ethics (Orchard & Radke, 2023).AI is also being utilized as a teaching assistant in STEM classrooms. Squirrel AI, an adaptive learning system in China, has been used to provide real-time feedback to students in mathematics and physics, significantly improving their problem-solving efficiency and retention rates (Luo, 2023). AI-powered teaching assistants like Jill Watson, developed at Georgia Tech, have demonstrated the ability to answer student queries efficiently in online courses, reducing educators' workload while increasing student engagement (Taneja et al., 2024). Such applications highlight AI's potential to serve as a valuable assistant in structured, logic-driven subjects.Beyond the classroom, AI is transforming STEM research via predictive analysis and automation. In biomedical engineering, for example, AI-powered tools analyze medical imaging and predict disease patterns (Burri & Mukku, 2024). In engineering, AI supports computational modeling and rapid prototyping, allowing researchers to conduct complicated simulations that would otherwise take months (Subramonyam et al., 2021). AI-powered research labs at MIT and Stanford have already begun utilizing AI-driven robotic experiments, which reduce the time needed for hypothesis testing and enhance the precision of experimental outcomes (Gower et al., 2024). However, studies found that while AI supports efficiency in STEM learning, overreliance on automated solutions led to the loss of students' ability to analyze and solve problems on their own, provoking concerns over the long-term cognitive impact (Çela et al., 2024).application in non-STEM education is contentious. Although AI-assisted writing software enhances grammatical accuracy, studies show that it doesn't enhance creative and critical thinking (Rahmi et al., 2024). Furthermore, AI-powered software that analyzes literature or historical texts cannot grasp cultural context and ethical sensitivity (Shehu, 2024). Unlike STEM's structured problem-solving, non-STEM disciplines entail subjective interpretation, and AI is not as effective if applied in independent learning (McIntosh et al., 2024).Recent studies have also examined AI's role as a teaching assistant in humanities and social sciences. AI-driven discussion facilitators have been utilized in some institutions to assist online philosophy and ethics courses by summarizing discussion points and proposing further investigation (Aleynikova & Yarotskaya, 2024;Baiburin et al., 2024). However, such systems are not deep enough for abstract discussion. Similarly, AI-assisted legal research platforms, e.g., ROSS Intelligence, have enabled more efficient case law analysis but cannot provide contextual insight, which continues to be essential in legal education (Migliorini & Moreira, 2024;Mohamed et al., 2024). These studies highlight that while AI might assist procedural aspects of non-STEM disciplines, its failure to comprehend the context and perform interpretative reasoning necessitates continued human intervention to ensure the depth and complexity of learning.AI in education presents a sharp contrast between STEM and non-STEM disciplines. In STEM education, AI boosts structured problem-solving, simulation, and automatic grading, with measurable gains in efficiency and accuracy. AI teaching assistants such as Jill Watson and Squirrel AI have already succeeded in STEM subjects by demonstrating accurate response capability, adaptive feedback, and performance analysis in real-time. Such structured environments are exactly where AI excels in pattern recognition, data processing, and structured problem-solving. There is, however, worry that over-reliance on AI tools will erode the capacity for independent critical thinking among students (Çela et al., 2024;Jia & Tu, 2024).On the other hand, AI is unable to fully replicate human reasoning in non-STEM fields, where subjective interpretation, morality, and creativity take center stage. While AI-powered discussion facilitators and legal research tools (ROSS Intelligence) make information more accessible and automate citation, they fall short of deep interpretation and contextual understanding (Migliorini & Moreira, 2024). The need for high-level, human-led critical engagement in fields such as literature, law, and philosophy underscores AI's limitations in abstract reasoning and moral decision-making (Jia & Tu, 2024). These asymmetries dictate that AI can only be used as a complementary tool and not a substitute for human instruction in non-STEM fields.From an ethical perspective, the role of AI in education brings questions of algorithmic bias and fairness. AI-augmented grading systems in STEM have been shown to be less subjective, yet the literature also reports racial and gender biases in AI-based grading (Chinta et al., 2024;Mangal & Pardos, 2024). Similar problems in humanities and non-STEM work, where AI-generated historical analyses and text-based responses have included false-making citations or miscontextualization in cultural contexts (Mandal et al., 2024;Papadopoulos et al., 2024). These results call for transparency, fairness, and human involvement in AI-assisted learning. However, to progress its use in education, interdisciplinary collaboration will be necessary as AI evolves. Pumpkin must find a middle-ground solution that combines automation with human-led insights to close the divide between STEM and non-STEM uptake of AI. It is essential that educators, policymakers, and AI technologists collaborate to integrate AI tools responsibly and equitably into education, focusing on critical thinking, ethical decision-making, and equitable access to technology.We must balance efficiency with cognitive engagement. In STEM fields, where AI augments problem-solving, hybrid learning environments mitigate against overreliance by coupling AIpowered support with instructor-facilitated reasoning exercises. AI-based digital tools should not present students with answers without clearer insight into the underlying logic that leads to those answers (Bhat & Long, 2024), ensuring that students engage critically rather than passively. Selfregulated learning strategies, like asking students to reflect on AI-generated solutions, have the potential to build up independent problem-solving skills.In non-STEM disciplines, AI's limitations in interpretative reasoning necessitate context-aware tools that incorporate cultural, ethical, and historical nuances (John-Mathews, 2021). AI discussion facilitators should evolve beyond summarization, using Socratic-style questioning to encourage deeper analysis. AI-assisted peer learning can also enhance engagement by allowing AI to structure discussions while instructors guide critical debates.Algorithmic transparency and fairness are absolutely important across disciplines. AI systems should be interpretable and should provide insight into how a particular conclusion has been reached. Other actions can be undertaken to inform AI committees within universities of AI oversight activities, enabling them to freely review AI-based assessments and discern bias. This, alongside its integration within curricula, will not only cultivate individuals with responsible AI use in mind, but also sharpen their critical faculties towards understanding the content produced by AI; ultimately, this is vital to ensuring proper equilibrium in the human-AI partnership.Improving the integration of AI will allow education to benefit from AI without sacrificing critical thinking, creativity, and ethical judgment. Further studies should prioritize improving the interpretability of AI whereby the supportive nature of AI is preserved in reference to human intelligence.Artificial intelligence has transformed education, streamlining solving structured problems but complicating critical interpretation of answered questions and moral analysis of problems. AI has found a comfortable home amongst the gradually data-heavy and more systematic STEM disciplines, thus far outstripping the acute pressure towards interpretative and creative learning that highlights the need for human authority around how such an alphabet can be used in education. Responsible AI deployment is about ensuring that it stays a tool that enables collaboration, as opposed to a human replacement.To counterbalance these challenges, institutions can promote equitable and effective AIinfluenced education with AI literacy initiatives that enable students and educators to evaluate AIgenerated output critically. AI governance frameworks will be crucial here too, ensuring we maintain fairness, transparency, and remove bias from grading and evaluation. Moreover, tackling the digital divide and guaranteeing that AI-powered learning tools are available to all pupils is key to avoiding educational inequalities.Going forward, collaboration between educators, AI developers, and policymakers will be essential to ensure that AI applications in education are improved and applied properly. Subsequently, future research should focus on improving the interpretative capabilities of AI, enabling it to support both structured and creative dimensions of learning.Ultimately, AI's success in education depends on ethical governance, human-centered design, and pedagogical innovation. By maintaining a balanced AI-human synergy, education can harness AI's benefits without compromising critical thinking, creativity, and ethical judgment.