In a groundbreaking exploration at the intersection of education and artificial intelligence, recent research delves deeply into the collaborative dynamics between human teachers and AI-driven educators within hybrid intelligence learning environments. This pioneering study articulates a nuanced framework designed to evaluate such human–AI collaboration, leveraging the Synergy Degree Model (SDM), a conceptual tool that captures the intricate interplay and mutual reinforcement between human and artificial agents managing educational experiences side by side. The researchers’ work marks a significant advance in understanding how hybrid intelligence configurations can transform classroom interactions, pedagogy, and ultimately, learning outcomes.
At its core, the investigation identifies and measures two critical dimensions of collaboration: the order degree and the synergy degree. The order degree reflects the structural alignment and coherence among subsystems involved in the collaborative teaching ecosystem, while the synergy degree quantifies the efficacy and harmony of cooperative interactions between human and AI actors. Together, these metrics reveal the delicate balance and interdependence that must be nurtured to maximize the benefits of integrating AI entities into traditional teaching frameworks.
This multifaceted assessment is anchored in real classroom data, drawn from environments where human teachers work in tandem with AI-powered educational robots. Through detailed observation and video analysis, the researchers dissect how collaboration unfolds across three interdependent subsystems: the collaboration subject (human and AI participants), the collaborative process (interaction patterns and coordination), and the environmental context (technological and physical setting). Each subsystem plays a vital, complementary role, influencing the overall performance of the hybrid intelligence team.
One of the striking revelations of the study is the dynamic nature of both the order and synergy degrees throughout the teaching segments. These dimensions are not static; they shift in response to the nature of teaching content and the activities designed for the classroom. For instance, more structured or interactive lessons tend to enhance coordination and collaborative fluency, while less defined formats present challenges for maintaining optimal synergy. This insight carries profound implications for curriculum designers and educators aiming to tailor AI integration effectively.
The practical applications emerging from this research extend beyond theoretical modeling. The authors envision educational practitioners employing the SDM-based evaluation framework as a foundation for developing real-time analytic dashboards. Such tools could provide continuous feedback about the state of human–AI collaboration, enabling educators to intervene promptly and adjust pedagogical strategies. Enhancing the order degree between subsystems through such feedback loops could unlock untapped potential in hybrid teaching models, catalyzing more seamless and fruitful human–AI partnerships.
However, the study does not shy away from acknowledging challenges and limitations intrinsic to current hybrid intelligence learning environments. Presently, the hybrid configurations examined revolve predominantly around human teachers and AI-enhanced educational robots, representing just one manifestation of human–robot collaboration in education. This specificity introduces potential biases in the findings, underscoring the necessity for broader investigations spanning diverse types of intelligent educational settings to validate and generalize the insights.
Moreover, the methodology relies heavily on manual video analysis, a labor-intensive approach that constrains the immediacy and scalability of assessments. This constraint implies that educators receive evaluative feedback with significant delays, curtailing opportunities for instant pedagogical refinement. Addressing this bottleneck, future inquiries should prioritize the development of automated, real-time analytic systems capable of continuously monitoring and evaluating human–AI collaboration dynamics without imposing heavy manual workloads.
Beyond observable behavioral metrics, the study spotlights the need for a more holistic understanding that encompasses internal cognitive and emotional dimensions of collaboration. Current evaluations focus primarily on externalized actions and coordination patterns, but human and AI agents alike are influenced by complex mental states and affective factors. Future research agendas are encouraged to probe the interplay between cognition, emotion, and collaboration quality within hybrid intelligence learning environments, aiming to elucidate how these intangible elements shape educational interaction and outcomes.
Importantly, the study surfaces an essential gap in understanding the underlying mechanisms that govern human–AI collaborative processes in classroom teaching. While the evaluation framework elucidates observable patterns of synergy and order, the internal dynamics—how decisions are negotiated, trust is established, and roles evolve between human and AI participants—remain largely unexplored. Comprehensive models detailing these internal collaboration mechanisms could empower the design of more adaptive and context-aware AI teaching assistants.
Furthermore, the moderate synergy degree observed in the current study suggests room for improvement. Achieving high-impact integration of AI in education demands not only technological advancement but also refined strategies for fostering fluid and productive human–AI interactions. This encompasses pedagogical training, interface design, and the cultivation of mutual understanding between humans and AI agents. Through iterative research and refinements, hybrid intelligence classrooms could evolve into highly responsive ecosystems that amplify both teaching effectiveness and learner engagement.
The implications of these findings resonate profoundly beyond the immediate classroom context. As AI technologies increasingly permeate educational settings, frameworks like the SDM provide essential lenses for critically assessing the quality and potential of hybrid intelligence collaborations. They equip educators, technologists, and policymakers with actionable insights to guide responsible AI integration, balancing innovation with pedagogy and human values.
In sum, this research marks a seminal step toward operationalizing the science of human–AI collaboration in education. By quantifying complex interactive phenomena through the order and synergy degrees and highlighting the systemic dependencies embedded within hybrid intelligence environments, the study charts a course toward more symbiotic partnerships between teachers and AI. As the field matures, such frameworks will likely underpin next-generation educational technologies and practices tailored to the complexities of real-world classrooms.
Looking ahead, the visions of this study point to a future where classrooms blend the best of human intuition and AI’s analytical power. The intelligent educational ecosystem envisioned here promises not only to optimize traditional teaching methods but to foster novel modalities of learning personalized to diverse student needs. However, realizing this promise demands sustained interdisciplinary research efforts, integrating insights from education, cognitive science, AI, human–computer interaction, and social sciences.
Ultimately, the evolving narrative of education amid the rise of hybrid intelligence highlights deep questions about the roles humans and machines will assume collaboratively. The knowledge generated by this line of inquiry equips the global community with evidence-based approaches to navigate these shifts thoughtfully and ethically. In doing so, it safeguards education’s human centricity while embracing AI’s transformative capabilities for learning enhancement.
As the early exploratory results unfold, there is palpable excitement about the possibilities that refined human–AI collaboration metrics could unlock for education worldwide. With iterative refinement, automated analytic tools, and expanded research scopes, the powerful synergy between human educators and AI companions can be harnessed to nurture more adaptive, engaging, and effective learning environments suited to the complexity of the 21st century.
Subject of Research: Human–AI collaboration in hybrid intelligence learning environments, focusing on evaluating synergy and order degrees within classroom teaching supported by educational robots.
Article Title: Examining human–AI collaboration in hybrid intelligence learning environments: insight from the Synergy Degree Model.
Article References:
Kong, X., Fang, H., Chen, W. et al. Examining human–AI collaboration in hybrid intelligence learning environments: insight from the Synergy Degree Model. Humanit Soc Sci Commun 12, 821 (2025). https://doi.org/10.1057/s41599-025-05097-z
Image Credits: AI Generated
