What is Human-AI Teaming in Three Levels of Complexity in Learning Environments?

Ray Hao is a PhD student and Fulton Fellow in Human Systems Engineering at Arizona State University, studying under Dr. Jamie Gorman.

Lucrezia Lucchi is a Psychology PhD student in the Dynamics of Perception, Cognition, & Action Lab at Arizona State University. Lucrezia has a background in Exercise Physiology and Human Movement Sciences.

Professor Jamie Gorman is an expert in modeling and measuring coordination dynamics in human and human-machine teams in The Polytechnic School at Arizona State University.

In today’s rapidly evolving digital world, parents are faced with growing questions about how to provide the best education for their children. One increasingly important factor in education is Human-AI teaming where students collaborate with artificial intelligence (AI) technologies to enhance learning. But what does it actually mean for students and AI systems to collaborate as teams, and just how complex can this process be?

What is Human-AI Teaming in Learning Environments?

Human-AI teaming in learning environments refers to the collaboration efforts of humans (teachers, students) and AI systems. It aims at enhancing educational outcomes by combining the unique strengths of both. In these settings, AI systems do not replace humans; instead, they work side by side with teachers and students to support and improve the learning process. Human-AI teaming can vary in complexity, starting with basic AI assistance and evolving into more collaborative teamwork and community, each offering distinct opportunities to enhance learning.

Level 1: Basic AI Assistance – Personalized Learning

At the foundational level, AI helps students by scaffolding personalized learning experiences and guiding students through customized learning experiences. This approach focuses on individual optimization by helping students progress at their own pace. Many tools on this level offer feedback, hints, and explanations tailored to the student’s needs but without direct group collaboration in the learning process.

Example: Duolingo, for instance, provides an adaptive and interactive experience where users progress through language lessons tailored to their learning pace. It adjusts the difficulty of lessons based on user performance, offering targeted hints and explanations to address specific challenges. Similarly, Khan Academy personalizes learning in subjects like math and science, suggesting exercises that align with the learner's current understanding. The platform offers immediate feedback, allowing users to correct errors in real-time and supporting a structured, individualized learning journey.

Level 2: Collaborative AI Partners – Learning Together

At this level, AI not only tutors students but also encourages collaboration in the learning process. It works alongside students in group projects, providing real-time insights, asking guiding questions, and even learning from interactions. By encouraging critical thinking and teamwork, AI makes learning more interactive and engaging. The Institute for Student-AI Teaming (iSAT) envisions classrooms where AI and students collaborate on problem-solving tasks, helping each other through challenging concepts while developing critical thinking skills (D’Mello et al., 2024).

Example: Our AI partner CoBi (Community Builder) is a great example of this. Designed for classroom use, CoBi helps groups of learners improve their collaboration skills by focusing on how they interact and work together. Another example is Kahoot! Team Mode, which supports AI-powered analytics to adjust in real time according to the group's collective performance. This platform has become a popular tool among educators to enhance students' collaboration and teamwork skills.

Level 3: AI-Enhanced Communities – Knowledge Building

At this level of complexity, AI is deeply integrated into the classroom environment, working as both a facilitator and a teammate for collaborative learning. It assists teachers in managing class-wide discussions, offers insights into student participation, and highlights key moments of critical thinking or engagement, nurturing an inclusive learning community where every voice is valued (Langer-Osuna, 2017).

Example: iSAT’s AI partner JIA (Jigsaw Interactive Agent) is designed to enhance collaborative learning by supporting student interactions and promoting effective group dynamics through real-time prompts and interventions. JIA encourages students to actively listen, share ideas, and build on each other's contributions in jigsaw activities, fostering deeper engagement and understanding. IBM Watson Education Classroom also offers an additional approach to personalized learning by helping teachers monitor and manage content, providing insights into student participation and needs to support learning outcomes for the entire class.

Potential Challenges & Concerns

As technology continues to evolve, we can expect even greater collaboration between humans and AI to enhance the way students learn, making education more personalized and effective than ever before. Despite the potential benefits, such as tailored learning experiences, improved collaboration skills, and increased accessibility to educational support, there are also challenges and concerns associated with Human-AI teaming.

One major concern, according to Alrazaq and colleagues (2023), is the risk of over-relying on AI, which could hinder the development of critical thinking and creativity in both teachers and students if not used in a balanced way. For example, students may become accustomed to relying on AI for quick answers or guidance, which can impede their capacity for thorough research and independent insight formation, potentially diminishing critical faculties. Similarly, teachers might depend on AI gaining insights for student assessment and feedback, potentially bypassing deeper observation and reflection on individual learning needs. This reliance can deter students from developing skills that are crucial for academic and professional success (see the studies of Koos & Wachsmann, 2023, and Zhai and colleagues, 2024, respectively). Navigating the use and deployment of AI in education also poses significant challenges, as integrating AI systems for personalized learning and automated assessments can lead to inconsistencies when evaluating students' progress compared to traditional methods. Additionally, privacy concerns arise as AI systems collect and analyze student data in various ways, raising questions about how this data is used and protected. Lastly, there is the issue of equity. The National School Boards Association defines educational equity as “the intentional allocation of resources, instruction, and opportunities according to need, requiring that discriminatory practices, prejudices, and beliefs be identified and eradicated.” Not all students have equal access to technology, and if Human-AI teaming becomes central to education, it could widen the digital divide between those with access to high-quality AI tools and those without. Ensuring that these tools are appropriately integrated as school resources requires widespread education on their availability and growing relevance to academic curricula.

Why Human-AI Teaming Matters for Our Children

Incorporating AI into learning environments isn't just about optimizing test scores – it is about preparing students for the future. As AI continues to evolve, the ability to work alongside AI partners will be a crucial skill. Educational research highlights that interactive and collaborative approaches to learning are the most effective, supporting the goal of incorporating AI to help students adaptively solve real-world problems, rather than focusing solely on individual mastery of narrow topics, while also developing critical thinking and teamwork skills that are vital for success in today’s workforce (see NASEM, 2018, Fiore and colleagues, 2018, and D’Mello and colleagues, 2024, for more information). By teaming up with AI at multiple levels of complexity, students have an additional platform for learning to collaborate effectively, think critically, and creatively problem-solve. These collaborative experiences empower students to succeed in the classroom and beyond, equipping them with the skills they need to navigate an increasingly complex and technology-driven world.

AI has entered the mainstream in classrooms, and there are different visions for how AI should be used to educate students. One vision, according to Vee (2024), is to replace human teachers with bots that are subject matter experts, capable of teaching any subject. Another approach, which we embrace, is human-AI teaming, in which students and teachers team with AI to enable new concepts of learning. We feel this latter approach may better engage learners with each other and their teachers by supporting collaboration, rather than students learning to interact primarily with closed, AI-based systems that may lack the richness and creativity of human interaction.

References

Abd-Alrazaq, A., AlSaad, R., Alhuwail, D., Ahmed, A., Healy, P. M., Latifi, S., ... & Sheikh, J. (2023). Large language models in medical education: opportunities, challenges, and future directions. JMIR Medical Education, 9(1), e48291. doi:10.2196/48291

D'Mello, S. K., Biddy, Q., Breideband, T., Bush, J., Chang, M., Cortez, A., ... & Whitehill, J. (2024). From learning optimization to learner flourishing: Reimagining AI in Education at the Institute for Student‐AI Teaming (iSAT). AI Magazine, 45(1), 61-68. https://doi.org/10.1002/aaai.12158

Fiore, S. M., Graesser, A., & Greiff, S. (2018). Collaborative problem-solving education for the twenty-first-century workforce. Nature Human Behaviour, 2(6), 367-369. https://doi.org/10.1038/s41562-018-0363-y

Koos, S., & Wachsmann, S. (2023). Navigating the Impact of ChatGPT/GPT4 on Legal Academic Examinations: Challenges, Opportunities and Recommendations. Media Iuris, 6(2). https://doi.org/10.20473/mi.v6i2.45270

Langer-Osuna, J. M. (2017). Authority, identity, and collaborative mathematics. Journal for Research in Mathematics Education, 48(3), 237-247. https://doi.org/10.5951/jresematheduc.48.3.0237

National Academies of Sciences, Division of Behavioral, Social Sciences, Board on Science Education, Board on Behavioral, Sensory Sciences, ... & Practice of Learning. (2018). How people learn II: Learners, contexts, and cultures. National Academies Press.

National School Boards Association. (n.d.). Center for Public Education: Equity. Retrieved November 6, 2024, from https://www.nsba.org/Services/Center-for-Public-Education#:~:text=Equity,beliefs%20be%20identified%20and%20eradicated.

Vee, A. (2024). AI pioneers want bots to replace human teachers - here’s why that’s unlikely. The Conversation. Retrieved 10/10/2024 from https://theconversation.com/ai-pioneers-want-bots-to-replace-human-teachers-heres-why-thats-unlikely-235754.

Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review. Smart Learning Environments, 11(1), 28. https://doi.org/10.1186/s40561-024-00316-7