If you’re reading this article, it seems you’re contemplating the possibility of applying to a PhD program, perhaps even collaborating with me. If this is the case, I encourage you to finish reading this piece, as it might address some of your questions.

The very first question you might have is likely: are you accepting PhD students for Fall 2024? The answer is yes. I accept PhD students in two different programs: the Educational Studies/learning technologies program and the Combined Program in Education and Psychology. Students interested in interdisciplinary research might also want to consider the Design Science program, where they would need to identify another advisor from a different discipline. Given my research direction, it is likely that the second advisor will be from the School of Information or Computer Science and Engineering. The admissions of these three programs are entirely independent from each other, so if you wish, you may apply for more than one program.

The second question usually pertains to my ongoing research: what kind of research do you do? And relatedly, what kind of students would make a good fit? While you may find more detailed information on the Research page, I’ll provide an overview from the perspective of prospective PhD applicants. My current research will include two broad and somewhat interconnected directions, and I will lay out my specific expectations for students within each of these directions:

Direction 1: Cognitive and Psychological Impact of AI on Young Children

This direction entails the design and research of child-centric AI tailored for young children, particularly those in preschool or early elementary schools. We collaborate with media producers such as PBS KIDS and Sesame Workshop to integrate AI into their existing educational content, aiming to enhance engagement and educational value. Within this sphere, we investigate how children interact with, learn from, and perceive AI-enabled educational media. Our approach typically involves experimental design, comparing children’s learning and interactions with AI possessing varying design features or even with human counterparts. Furthermore, we also focus on how AI might influence children’s perception of the mind or imbue AI with human-like attributes.

Given this emphasis, a strong background in developmental psychology or cognitive science is desirable. This would involve familiarity with developmental theories and experimental methods. Students with a strong human-computer interaction background who can independently develop functional research prototypes (which typically involves using API or other research-developed models) could also make valuable contributions. If you are interested in this direction, you might want to take a look at these two sample papers to gain a better understanding of the styles of our work.

Talking with machines: Can conversational technologies serve as children’s social partners?

Dialogue with a conversational agent promotes children’s story comprehension via enhancing engagement

Direction 2: Teacher-AI Collaboration to Support Teaching and Learning

This direction revolves around the integration of AI within classrooms to enhance teaching and learning. Specifically, we’re creating a platform enabling teachers to leverage generative AI for formulating questions related to science reading materials or video resources. We’re devising mechanisms that empower teachers to stay in the loop, allowing them to review, edit, and refine questions generated by AI to align with their instructional needs. We’re exploring how this collaboration between teachers and AI might impact teacher workflows, classroom instruction, and student learning outcomes. Additionally, we’re in discussions for a potential collaboration with PBS to scale up this project. In particular, we are exploring ways to develop AI that can support teachers in engaging students in discussions around the social implications of science and technology topics, as a way to facilitate connecting social justice issues with STEM education and to make science more relevant to students.

Within this direction, I’m seeking candidates with a strong foundation in NLP, who could facilitate coordination with our computer science collaborators. I am also seeking candidates who have classroom teaching experience in the U.S., preferably in STEM-related subjects, and are passionate in studying how AI can empower education practitioners. If you want to learn a bit more, here’s a brief summary of a project.

Building a Teacher-AI Collaborative System for Personalized Instruction and Assessment of Comprehension Skills

If any of the aforementioned points resonate with you and solidify your interest, the third question that might come is: should I reach out to you before submitting an application? The answer is: it is not necessary and it is entirely up to you. Your application materials will already provide sufficient insight into your background and interests, and both the admissions committee and I will carefully review your materials, regardless of whether we’ve had prior contact.

I hope this piece will provide some assistance. I look forward to the privilege of getting to know you better throughout the application process.