In the rapidly changing field of AI, education is at the forefront. New AI tools are constantly emerging for educators and students, from AI tutors to curriculum creators, leading to a surge in the AI education market.
However, the long-term effects of AI use on students are still unknown. As educational AI research tries to keep pace with AI development, questions remain about the impact of AI on student motivation and overall learning. These questions are particularly important for students of color, who face more systemic barriers than their white peers (Frausto et al., 2024).
Emerging in the aftermath of the COVID-19 pandemic and the resulting decline in student learning and motivation, AI encompasses a wide range of technologies, including tools like ChatGPT, which use vast data repositories to make decisions and solve problems. These tools can assist with tasks such as generating essays from prompts, leading students to quickly adopt them in the classroom. While educators and administrators have been slower to embrace these technologies, they are now using AI to manage unregulated student usage and streamline their work with AI-powered grading tools. Despite the controversy surrounding the use of AI in education, it is clear that it is here to stay and rapidly evolving. The question remains: Can AI enhance students’ motivation and learning?
A recent rapid review of research found that students’ motivation is influenced by their experiences both in and out of the classroom. While the review emphasizes that student motivation is shaped by more than just individual attitudes, behaviors, beliefs, and traits, it does not fully address the effects of AI on student motivation (Frausto et al., 2024).
To understand how AI may impact the motivation and learning of students of color, we must examine the nature of AI itself. AI learns and evolves based on preexisting datasets, which often reflect societal biases and racism. Relying on biased data can result in skewed and potentially harmful outputs. For example, AI-generated images may perpetuate stereotypes, such as exclusively depicting leaders as white men in suits. If AI were used to create a leadership curriculum, it might produce content that aligns with this stereotype. This not only reinforces the stereotype but also creates content that students of color may find unrelatable, leading to disengagement and loss of motivation in the course (Frausto et al., 2024).
This does not mean that AI is the only potential source of bias. Discrimination is a persistent factor in the real world that affects students’ motivation and learning experiences. Similar bias has been observed in non-AI learning and motivation tools created based on research focused on predominantly white, middle-class students (Frausto et al., 2024). AI simply reflects the biases present in the broader world and education sector; it learns from real data, and the biases it perpetuates mirror societal trends. AI biases are not mysterious; they are a reflection of our own biases. Teachers also exhibit comparable levels of bias to those in the world around them.
When considering current AI use in education, these inherent biases can be cause for concern. AI has shown subtle racism in the form of dialect prejudice, where students using African American Vernacular English (AAVE) may receive less favorable recommendations from the AI they interact with compared to their peers. Similarly, teachers may be influenced by bias in the grades assigned by AI-powered programs, favoring the phrasing and cultural perspectives of white students over those of students of color. These examples highlight the biases present in current AI use in education, raising alarm bells. Similar instances of discrimination between humans, such as from teachers and peers, have been linked to decreased motivation and learning in students of color (Frausto et al., 2024). Thus, AI and its biases may present another hurdle that students of color must overcome; AI learning tools designed for and tested on white students may have negative effects on students of color due to inherent biases.
To combat these biases, we recommend incorporating anti-bias practices. With AI, there is an opportunity to integrate bias awareness and anti-discriminatory practices. Addressing bias in AI systems development has been a key focus for several years, with companies like Google releasing AI guidelines that prioritize addressing bias. Intentionally selecting diverse datasets to train AI and rigorously testing them with diverse populations can help ensure equitable outcomes. However, even with these efforts, AI systems may still exhibit bias toward certain cultures and contexts. Despite having good intentions to support student learning and motivation, AI may inadvertently lead to negative outcomes for underrepresented groups.
While AI integration in education is rapidly advancing, there is an opportunity to address and understand the potential for bias and discrimination from the start. Although we cannot definitively determine AI’s impact on the motivation and educational outcomes of students of color, research indicates bias as a potential obstacle. By approaching the implementation of AI in education with intentionality, inclusivity, and awareness of potential harm, we can strive to create an AI-powered learning environment that enhances the educational experiences of all students.
Eliana Whitehouse, EduDream
Eliana Whitehouse is a macro social worker with experience in supporting community-based initiatives and research throughout the lifespan. Currently, she is a Research and Evaluation Associate at EduDream, a Latina-founded, women-owned education research consulting firm.
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