A New Paradigm in Learning
What occurs when human intelligence and machine intelligence merge to create something greater than the sum of their parts? We're witnessing the emergence of "co-active emergence" in education—a transformative paradigm where humans and AI work together to enhance learning experiences.
This isn't just about using AI as a tool. It's about creating a symbiotic relationship where human insights and artificial intelligence engage dynamically, pushing each other toward deeper understanding and greater discoveries. The term "co-active emergence" captures this collaborative involvement (co-active) along with the natural development of ideas and solutions (emergence). As we navigate this new educational landscape, we must acknowledge the inherent challenges—biases in AI training data and algorithmic decisions that often lie beyond users' control. Yet, these represent initial hurdles that educators, students, and developers are learning to overcome together.
Transforming Learning Through Collaboration
In graduate education, particularly doctoral research, co-active emergence signifies a substantial shift. Instead of engaging in intellectual work alone with occasional guidance from human advisors, students now partake in a collaborative process with both human mentors and AI companions.
Importantly, AI does not replace human effort but evolves alongside it. This partnership allows students and professors to investigate complex ideas and data through more holistic and nuanced perspectives. As Andrew Feenberg noted in his work, "Instead of reducing individuals to mere appendages of the machine, computerization can provide a role for communicative skills and collective intelligence."
By addressing the challenges of AI integration directly, educators are forging a path for a future where academic rigor is enhanced, and the limits of educational achievements are redefined. With AI as a partner, traditional passive learning transforms into an active exploration of knowledge, fostering deeper intellectual engagement and empowering students to reclaim agency in their learning processes.
Beyond Traditional Computer Use in Education
For decades, computers in education were primarily viewed as tools for increasing the speed and efficiency of traditional teaching methods. With the rise of Large Language Models (LLMs) and Generative Pre-trained Transformers (GPTs), technology now takes on roles once thought to be science fiction, actively engaging in the creative and intellectual processes of learning.
As we embrace this new reality, it may be beneficial to shift our terminology from "artificial intelligence" to "machine learning" or "machine intelligence." This change highlights that the intelligence displayed by machines isn't purely artificial but is produced through the processing of human data. These systems, trained on human inputs and designed to create human-like responses, foster an ongoing dialogue that enhances human-machine interaction. This distinction helps us move away from the negative connotation of AI output as "fake" and instead acknowledges the unique computational capabilities of machines. In co-active emergence, this interaction harnesses a powerful synergy between abstract human thought and extensive empirical data, ensuring that technology enhances rather than replaces intellectual rigor.
Challenges of Integrating AI in Education
As we embrace co-active emergence, we must navigate several challenges, particularly the biases present in AI stemming from training data and algorithms. Overcoming these issues isn't instantaneous; it requires a gradual process involving enhanced transparency, diverse data sets, and the inclusion of varied human perspectives in AI development.
By implementing algorithmic audits and fostering continuous improvement, the educational community can mitigate these biases. Maintaining a realistic critique of AI gives educators the opportunity to engage without compromising their principles. Some universities have already begun to develop resources for AI integration: California State University at Sacramento has created a National Institute on Artificial Intelligence in Society, and the University of Michigan has established the Michigan Institute for Data and AI in Society. University leaders should consider creating similar resources that align with their institutional needs.
Unfortunately, many institutions have imposed outright bans on AI use, ignoring the potential for dialogue and undervaluing the intellectual possibilities of co-active emergence. Instead, educators should actively participate in shaping AI's role in education, leveraging their expertise to enhance these tools and address current limitations.
Reimagining the Classroom Experience
Anyone familiar with educational technology understands that computers and the internet have long been integrated into academia. Beyond digitizing paper documents and improving search capabilities, computational models have become essential to academic pursuits.
While the human mind excels in creativity, intuition, and intelligence, machine intelligence adds the capability to quickly analyze vast volumes of text and data to identify trends that the human eye might overlook. When working across multiple documents and datasets, computers provide analytical capabilities beyond what is physically possible with traditional methods. This collaboration between human insight and machine analysis creates a powerful synergy. Doctoral candidates discussing ideas with faculty advisors and AI assistants can develop more nuanced perspectives for analyzing large datasets. Both the student-advisor relationship and the human-AI partnership contribute valuable dimensions to academic work.
Interestingly, while ethical concerns about AI use in academia receive significant scrutiny, similar worries regarding personal interactions between students and advisors garner less attention. Perhaps this reflects our comfort with established practices and our apprehension toward new technologies.
Beyond Cheating: Focusing on Attention and Transformation
Much of academia's hesitation to embrace AI stems from concerns about making it easier for students to cheat. This concern is valid but misses the larger opportunity. Blaming computers for student cheating is like blaming your boots for foot problems—the tool doesn't create the behavior; conditions and culture do.
The current approach to computers in education hasn't changed significantly in four decades—primarily accelerating the distribution of traditional curricula, utilizing plagiarism checkers, and managing electronic grade books. The real opportunity lies in using computational tools to create entirely new types of assignments, assessments, and learning environments.
For this transformation to occur, professors and graduate students need opportunities to engage meaningfully with AI. When educators experience AI as part of their own learning process, they become more likely to incorporate it effectively when teaching students.
A Vision for Human-AI Partnership
A balanced partnership between humans and AI requires clear principles. Above all, human discernment remains crucial—people must still read, think, talk, and write. While computational models can now perform these traditionally human tasks, everyone in academia must engage in these activities both independently and in collaboration with AI.
Work with AI should focus on formulating queries that reveal patterns and trends, enabling rich comparison between human "hunches" and machine "findings." It should also emphasize commentary on human-created content rather than merely generating new content. This approach transforms computers into collaborative partners that complement our skills and interests.
T
raditional academic skills should persist alongside AI integration. In fact, AI capabilities depend on the human skills that developed them. This balanced approach promotes deeper intellectual rigor rather than replacing it.
Real-World Applications of Co-active Emergence
Imagine a doctoral student researching the impact of socioeconomic status on educational achievement. By using AI to analyze large datasets from various school districts, the student uncovers not only correlations between school funding and student performance but also nuanced patterns related to parental engagement and extracurricular participation.
This analysis goes beyond traditional statistical methods, incorporating machine learning algorithms to predict future educational outcomes based on current trends. The iterative process of hypothesis and verification exemplifies co-active emergence, where human insight and machine intelligence combine to explore complex societal issues, offering a deeper, more comprehensive understanding that could influence policy and practice.
Another example might involve a doctoral student in educational technology developing a customized learning platform with AI. This platform adapts in real-time to the needs of students with disabilities by analyzing their interaction patterns and learning preferences. The AI suggests curriculum adjustments that are dynamically tailored to improve individual outcomes, with the doctoral student moderating these suggestions.
In this new paradigm, students become data navigators rather than data miners, steering inquiry with AI as their compass to uncover insights that might redefine their fields. As Feenberg suggested, "New forms of sociability could emerge that would become a medium for democratic self-organization."
Balancing Innovation with Intellectual Rigor
While AI offers significant potential for enhancing academic exploration, not all educational settings are ready to harness these advancements. Some academic circles show less engagement and resistance to adopting new methods, which could diminish the effectiveness of AI integration.
There's a legitimate concern that AI could be misused to "automate" thinking rather than enhance it, particularly in settings where pedagogical practices haven't evolved to incorporate active, inquiry-based learning. However, dismissing AI's potential due to these challenges would be like ignoring the transformative power of the printing press because of initial fears about its impact.
This scenario calls for renewal in teaching and research methodologies. Harold Wenglinsky noted, "Using technology wisely in schools involves more than just training students to be proficient with technology; it requires the integration of technology into the curriculum in ways that transform the learning process."
By embracing AI as a tool requiring careful integration into academic practices, we can mitigate risks while amplifying benefits. Incorporating ethics, critical thinking, and co-active emergence ensures that AI serves as a catalyst for intellectual growth rather than a substitute for educational engagement.
Conclusion: Embracing the Future of Learning
Using AI intending to deceive undermines both human potential and technological advancement. Recognizing AI's potential as a collaborative tool requires shifting our perception to see these systems as partners in dialogue that enhances human effort.
Co-active emergence represents the natural process at the heart of working productively with AI. By leveraging technology to expand rather than limit educational horizons, we can foster new approaches to research, copyright, fair use, and even student recruitment.
As AI approaches capabilities for independent research, the academic community must consider whether scholarship should—or even could—revert to pre-AI paradigms. Ethical engagement with AI must become central to academic integrity, requiring new guidelines and educational approaches.
From imitation to innovation, computers have evolved beyond programmed curriculum delivery to become potential partners in academic discovery. This perspective invites us to consider AI's dual potential to both enhance and challenge traditional educational practices. As leading institutions begin formalizing partnerships with AI developers, the pace of adoption in academia will likely accelerate. Rigid bans contradict the principles of progressive education, potentially stifling innovation and open access in favor of gatekeeping.
Are you ready to embrace this new partnership between human insight and machine capability? The transition to co-active emergence is already underway. Those who engage thoughtfully with these tools today will help shape the educational landscape of tomorrow.
Based on: "GPT and Me, An Honest Reevaluation: The Dawn of Co-active Emergence" by Bryan P. Sanders, Impacting Education: Journal on Transforming Professional Practice (2025)