Enhancing Student Learning and Personal Development in AI via a PBL Program
By: Shuhan Li & David Huang
What is Project Based Learning
PBL — short for project-based learning — enables students to explore and discuss an ongoing problem in the world. This inclusive approach drives students to learn more about current issues and prepares them to pursue related interests. This research explored the impact of a PBL program for AI education on students’ confidence, career interest, and general knowledge of the topic before and after participation. Our PBL program was adapted from the Fresh Squeeze data bias lesson plan, with additional group projects involving the Cozmo robot.
What we are analyzing
We carried out the program in a public middle school in suburban western Pennsylvania. One hundred four students from Grades 6 to 8 joined the program. The school provided access to STEM-related activities and coding software such as code.org and Booksans. As a result, students in our research already had basic technological literacy. Furthermore, the participants generally expressed interest in AI-related knowledge and activities. This helped yield more meaningful results and improve students’ understanding of AI. This study aimed to answer the research questions below.
RQ: Does participation in an AI PBL Course impact middle-school students:
a) overall interest in AI?
b) interest in AI-related careers?
c) knowledge of AI concepts?
How are we analyzing
We collected data via a 30-minute survey given to the students before and after the program. The survey consisted of 17 questions divided into 2 constructs: self-efficacy (9) and career interest in AI (8). These constructs measured students’ interest in, comfortability with, and attitude toward their capabilities to learn about AI and pursue relevant careers. The questions measured students’ efficacy and interest on a scale of 1 to 5, with one being “strongly disagree” and five being “strongly agree.” In addition to the 17 questions, there were five questions about their previous experiences with AI educational programs and/or coding courses and open-ended questions about their fluency in programming languages. Finally, the overall assessment of students’ AI knowledge was indicated with another 17 questions with a maximum score of 17 points.
Our Key Findings
This research measured the change in three areas: students’ confidence (i.e., self-efficacy), career interest, and knowledge assessment.
The PBL intervention provided students with a hands-on opportunity to work with AI. Rigorous as it was, the learning environment remained encouraging enough for students to demonstrate their full learning potential. By previous literature, this study found that PBL programs significantly improved students’ confidence in their ability to learn AI. An improved efficacy level could motivate students to enroll in advanced AI courses. In other words, a more confident student would be more likely to continue exploring AI-related knowledge in their future academic pursuits.
The research also showed how a hands-on learning experience could contribute to future career preparedness. The PBL program honed students’ teamwork, organization, problem-solving, critical thinking, and other core skills. These skills will assist students in both their academic and career endeavors. Students adept at these skills demonstrated an increase in their interest in related jobs. This proves that the availability of learning opportunities for students corresponds to their career interests. This research study corroborates the Social Cognitive Career Theory (SCCT), which draws the connection across students’ self-efficacy level, career interest, and contextual factors. In this study, efficacious students’ increased career interest is supported by SCCT. Another environmental factor to consider is the relationship between students and teachers. Instructors of the program were encouraging and personable to the students. For instance, many teachers held open discussions among students to share different perspectives. Some students may have even looked up to their instructors as inspiring role models. An inclusive and constructive classroom environment thus not only builds up students’ skills but also helps them envision themselves in AI-related career paths.
Regarding AI knowledge assessment, the PBL intervention increased students’ understanding of basic AI knowledge. Unlike a conventional classroom, the PBL program allowed students to learn from first-hand experience rather than textbook-based rote work. Students were encouraged to explore AI knowledge both individually and in groups. Additionally, students also apply learned knowledge to real-world problem-solving. Simply put, students learned about AI mechanisms and applications through the practical training in this PBL program. Students could significantly improve their academic performance, provided that PBL encourages them to innovate and generate social impact.
Impacts of the Study
This study has confirmed the benefits of a PBL program in facilitating students’ learning and socio-cognitive development. This study has profound implications in various aspects. For educators, school administrators, and other stakeholders, this study can inform them about innovative curriculum designs as they are implementing new AI education programs. They can also use the evidence from this study to lobby for more funding for PBL programs. This can include both small- and large-scale projects within the curriculum. New activities such as Teachable Machine can diversify the ways to learn about AI. Teacher education can also implement professional training of PBL instructors to better assist students in the classroom. Above all, the various forms of student-teacher interactions are worth recognizing. Formulating a strong bond with peers and instructors at a young age will generate meaningful long-term impacts on students’ personal growth. While the students of this study are very young and hence their career interests may fluctuate, an effective PBL program can still open up career possibilities for them, facilitating them to explore their career pathways gradually.
Future of the Study
Students today are living in a rapidly digitized world shaped by AI. This, in turn, reshapes the job market that requires data and technology literacy. The shifting landscape in today’s job market is what education institutions must prepare future generations for. The study presented the positive impacts an innovative PBL program could have on students. Based on the findings, we hope more and more institutes will adopt PBL in their classrooms to equip students with fundamental skills while stimulating their personal development.
Future studies could refine the methods in the following two ways. The first is to analyze the longer-term effects of PBL programs and similar opportunities students partake in. Because the participants are still very young, their career interests and knowledge level can easily change throughout the remainder of their education. Therefore, similar opportunities must be provided throughout the rest of their school years to sustain their interest in AI. Accordingly, a longitudinal study is necessary to track the changes at different stages of learning. Second, a more age-appropriate survey is needed to measure students’ confidence, career interest, and assessment more accurately. Many questions in the current questionnaire were too technical for students of a young age. Whilst this group of students had a decent amount of previous experience with technology, others may not. As a result, simplifying the wording for middle schoolers could increase the accessibility of the surveys and thus better reflect students’ overall qualities and knowledge levels.
Shuhan Li is a masters student at Teachers College, Columbia University. She currently studies International and Comparative Education. Shuhan also had an undergraduate degree in Economics and Social & Cultural Analysis at New York University. Shuhan specializes in educational research and transnational activism. Her research encompasses AI education, diversity and inclusion, intersectional politics, and sustainable development. She presented at EAAI 2023 with Jordan Mroziak about critical pedagogy for K-12 CS education. With ReadyAI, Shuhan has two research papers accepted as posters in the CITERS and AIED conferences. Shuhan is also the project manager at a start-up NGO for climate justice.
David Huang is an undergraduate studying Computer Science and Electrical Engineering at New York University. He has been involved in research and engineering projects alike surrounding AI and technology as a whole. With a foundation in multiple programming languages, David seeks to continuously explore how code powers innovation in our daily lives. Additionally, he is a teacher’s assistant at a local NYC grade school during the year and works with Ready AI in the summers to provide prospective opportunities for our future generations.
To learn more about ReadyAI, visit www.readyai.org or email us at info@readyai.org
References
Bell, S. (2010). Project-based learning for the 21st century: Skills for the future. The clearing house, 83(2), 39–43. https://doi.org/10.1080/00098650903505415
Condliffe, B., Quint, J., Visher, M. G., Bangser, M. R., Drohojowska, S., Saco, L., & Nelson, E. (2017). Project-based learning. A literature review. MDRC, 1–84. https://www.mdrc.org/sites/default/files/Project-Based_Learning-LitRev_Final.pdf
Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1): 79–122. https://doi.org/10.1006/jvbe.1994.1027
