Tsinghua’s early AI-taught courses reveal promising advantages and notable challenges.

“What I really want is to harness lift to create my own flying racecar,” says a cheeky engineering student in response to a question about dealing with aerodynamics. “That’s called a jet!” chimes in a boisterous classmate. Their teacher draws the students back on track, saying, “Whether it’s a jet or self-driving cars, we need to learn today’s content to build anything.”

This might seem like an ordinary tangent in a classroom discussion. But here’s the twist: In this case, only the first ambitious engineering student is human. The instructor and the classmate are both created by artificial intelligence, and are known as AI agents. These AI agents form part of a Massive AI-empowered Course, or MAIC, a project developed by an interdisciplinary team including computer scientists from Tsinghua University’s Department of Computer Science and Technology, as well as educational scholars from the Institute of Education.

Many teaching institutions are grappling with the challenge of students surreptitiously using large language models (LLMs) — the deep-learning algorithms behind chatbots — to do their homework for them. But this team is using LLMs to enhance learning by creating personalized lessons that tailor the content and the pace of the lesson to the individual student’s needs, while offering continuous AI guidance throughout the learning process.

Powered by General Language Model (GLM), a LLM fully developed by Tsinghua, MAICs assist instructors in designing courses by automatically generating teacher agents, teaching assistant agents, and peer agents with diverse styles. This allows for the rapid creation of a personalized multi-agent learning environment, where students can engage in bespoke online activities with the guidance of multiple AI agents.

Personalized Support at Scale

Before MAICs, there were traditional online courses offer web-enabled learning to large numbers of students. These courses often include videos, readings, and problem sets, along with interactions via user forums, social media discussions, and immediate feedback from quizzes and assignments. These online courses have played a key role in expanding access to education, offering opportunities for millions of learners worldwide.

MAICs, on the other hand, can be distributed widely and also offer personalized learning experiences. “We are applying LLMs to improve tertiary education,” explains Liu Zhiyuan, a computer scientist who led the MAIC creation project. As part of a new Tsinghua University AI-empowered education initiative, Liu’s team has tested two MAIC classes on hundreds of university students. The data suggested that students were highly satisfied with MAICs, as 84.1% expressed enjoyment in using them based on the Community of Inquiry (CoI) framework.

The MAICs also do not require advanced technical skills from either teachers or students, Liu adds, so the technology will make education more effective, accessible, and equitable. “It can reach students who might otherwise be deprived of opportunities, helping to bridge the educational gap,” he says.

The team envisioned MAICs as a new approach to interactive and personalized learning, which could help promote greater equality by improving access for individuals who may be unable to attend in-person classes due to geographical limitations, work commitments, or health issues. Additionally, MAICs are quick and cost-effective to set up, offering the potential for widespread adoption while maintaining affordable.

Each MAIC has an AI instructor, an AI teaching assistant, and multiple AI classmates, with different personalities. The developers say that MAICs are unique in that every aspect of the learning process, from teaching to answering students’ questions and facilitating discussions, is carefully conducted by the AI agents. A typical scenario involves the teacher AI dynamically adjusting instructional pace based on the student’s learning progress, deciding in real-time when to transition to the next topic during lectures.

At the same time, the teaching assistant AI and multiple AI classmates actively engage students with questions and foster discussions about the course material, thus creating an interactive and stimulating classroom environment that simulates the vibrancy of a traditional classroom. One advantage of this, says Liu, is that you’re engaging with a range of virtual peers who are deliberately designed to offer varied perspectives.

Meeting students where they are

The AI instructors do not just answer students’ queries but also analyze why a student might have posed that question and then adapt the course to their level and needs accordingly. The design of the instructor was modified based on student feedback, explains Yu Jifan, a computer scientist and education researcher at the Institute of Education at Tsinghua, who co-led the study. For example, if the system identifies an opportunity to enhance student's higher-order thinking, it might subtly increase the participation of certain inquiry-driven peer agents to encourage deeper analysis and reflection.

As mentioned previously, the team analyzed 100,000 learning records from more than 700 students taking two university-level classes, one on the topic of AI and one teaching study skills, over three months1. They also interviewed 20 students along the way. They found the AI-taught group slightly outperformed a human-taught control group in course tests.

But while test scores may have been good, there are ethical concerns about the adoption of LLMs in education, related to inaccuracy, discrimination, and security. One issue is that chatbots often ‘hallucinate’, confidently presenting incorrect statements as facts. To counter this, the MAIC’s AI instructors’ teaching output was regularly monitored by subject experts, and both students and teachers were able to flag obvious errors. LLMs also amplify biases inherent in their training data. To address this, the team attempted to weed out discriminatory data during the design stage, but admits these are ongoing challenges.

The team also encrypted student data for privacy. These ethical concerns are a priority, says Yu. “If there is a large amount of unsafe content, it is difficult to see how the system can provide equal educational opportunities for everyone,” he says.

Another worry is that students taught by AI might struggle with developing certain social skills. The team introduced the AI classmates to help, with some success, explains Liu. “AI classmates encourage students to participate in discussions as the students do not fear judgment from real peers,” he says. This confidence can then be carried over into real-world classrooms.

Scaling Up and Addressing Limitations

In 2023, Tsinghua University began piloting eight courses in science, engineering, and the social sciences using intelligent teaching assistants, which have been met with great enthusiasm from lecturers2. Since then, numerous leading scholars at Tsinghua have started exploring ways to further integrate AI into both their teaching and research. These ongoing efforts have not only bolstered confidence in the approach but have also positioned the project for future scaling and broader implementation, according to Liu.

The team plans to further scale up their MAIC by expanding the number of participants to a few thousand people so they can conduct more comprehensive evaluations and upgrades and are preparing a series of new features, such as automatic knowledge profiling for students. “We are also continually recruiting educators who are interested in this educational model to participate,” says Yu.

The team hopes to eventually expand to other universities across the country and worldwide. Scaling up should be easy, says Liu, since by design, LLMs can handle, and adapt to, many students simultaneously. Not every class can be converted to an MAIC, however. The system is best suited to introductory lessons and those that do not involved teaching hands-on skills.

That said, AI’s teaching capabilities are not without limitations. They may not be particularly adept at facilitating deeply subjective, abstract, and value laden questions, for example, they cannot really ‘understand’ the topics they teach. “AI tutors operate based on algorithms and data, lacking personal experiences, emotions and the intuitive insights that humans bring to the table,” says Liu. These include granular and multi-disciplinary knowledge, and more complex human analyses, such as the ability to quickly understand a students’ cognitive blind spots. Most of all: “AI cannot replicate the ability to inspire and motivate,” says Liu.

For these reasons, he says, AI-tutors will never replace human teachers. However, MAIC has the potential to enhance both the student and human teachers’ experiences.

Ideally, AI classes will remove the burden of teaching repetitive tasks, enabling human lecturers to focus on the creative and interpersonal aspects of teaching. “Human teachers can instead concentrate on cultivating students’ higher-order cognitive abilities,” says Yu. “I think the role of the human lecturer will become even more significant, not less.”

References

1. Yu, J. et al. From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents, preprint: arXiv.2409.03512 (2024).

2. “Tsinghua AI teaching assistant is here! Opening a new era of teaching,” Tsinghua University, 5 March 2024.

Editors: Ma Mingwei, Li Han