Abstract
A persistent achievement gap exists between students from lower and higher socioeconomic backgrounds, known as the knowledge gap. Students with less background knowledge and vocabulary start behind and struggle to keep pace as new content is introduced. This gap can be addressed by tapping into students’ intrinsic motivations and interests. When students are curious and engaged with reading materials, their comprehension improves. Generative AI tools provide an opportunity to dynamically create texts that build on students’ interests and background knowledge. This essay examines Carol Dweck’s growth mindset theory and the potential for using generative AI to target student interests. Keeping students motivated and immersed in high-interest reading material can stimulate the effort and persistence necessary to close knowledge gaps. With the right supports, all students have the potential for growth.
Questions to Consider:
- How can teachers effectively leverage generative AI tools in the classroom to improve engagement and comprehension while avoiding potential downsides?
- In what ways might tapping student interests fail to improve reading achievement, and how can educators mitigate these risks?
- How can an interest-driven approach using AI-generated content be combined with teaching of foundational literacy skills and reading strategies?
- What safeguards need to be in place to ensure AI systems reinforce principles of diversity and inclusion?
- Could a focus on student interests ever detract from building academic knowledge if not properly balanced?
Introduction
Education is intended to be the great equalizer, providing opportunities for students of all backgrounds to acquire knowledge and skills. Yet persistent gaps in achievement exist between students from lower and higher socioeconomic backgrounds. This phenomenon is known as the knowledge gap. Students who start school with less foundational literacy and vocabulary development tend to struggle when new content is introduced. Over time, this gap in background knowledge and skills widens, making it increasingly difficult for disadvantaged students to catch up to their peers. However, insights from psychologist Carol Dweck’s research on growth mindsets suggest an approach to help close this knowledge gap. By leveraging students’ intrinsic motivations and tapping into their interests through generative AI technology, reading comprehension can be improved in ways that help all students reach their potentials.
The Knowledge Gap
Decades of research have illuminated the tenacious knowledge gap between students from lower and higher socioeconomic backgrounds. Sociologist Basil Bernstein coined the term “knowledge gap” in the 1960s to describe how information and innovations tend to initially reach high-SES populations first (Tichenor, Donohue & Olien, 1970). In an educational context, the knowledge gap refers specifically to the disparity in academic achievement between students of different social classes.
On average, students from disadvantaged backgrounds begin school with less foundational literacy skills, vocabulary knowledge, and background knowledge about the world due to lack of early exposure and resources (Marulis & Neuman, 2010). However, school instruction builds on this prior knowledge. When new content is introduced, students who have stronger background knowledge and vocabulary are able to learn and retain the information more successfully (Cromley & Azevedo, 2007). In contrast, students without these foundations struggle to grasp new concepts and keep pace. Over years of schooling, this snowballs into widened gaps in literacy proficiency and content knowledge between lower and higher-SES students (Neuman, 2006).
Closing knowledge gaps is critical for social equity and providing all students the opportunity to fulfill their potentials. Interventions typically aim to fill gaps in background knowledge through tutoring, summer school, vocabulary instruction, and building knowledge of high-information text structures like novels and informational books (Marulis & Neuman, 2010). However, another approach is suggested by the research of psychologist Carol Dweck on intrinsic motivation and growth mindsets.
Growth Mindsets
Carol Dweck’s decades of research have revealed the immense power of students’ mindsets and beliefs about their own abilities and potential. Students who believe intelligence is a fixed trait exhibit a “fixed mindset,” linking effort and challenges with a lack of innate ability. In contrast, a “growth mindset” views intelligence as malleable, seeing effort and challenge as opportunities to expand one’s abilities (Dweck, 2006).
Students praised for effort and strategy are more likely to develop growth mindsets. They exhibit greater motivation, resilience, and achievement compared to students praised for inherent traits alone. Growth mindsets allow students to see setbacks as chances to improve rather than signs of permanent deficiency. Growth mindsets are associated with higher grades, while fixed mindsets predict steeper declines in grades over time, especially among at-risk student populations (Blackwell et al., 2007).
Most relevant to the knowledge gap is how growth mindsets increase students’ motivation for learning. Students who believe they can grow their abilities value learning opportunities more intrinsically. They put forth greater effort, persistence, and engagement in order to expand their knowledge and skills (Dweck, 2006). Tapping into this intrinsic motivation is key for supporting disadvantaged students in closing knowledge gaps.
Leveraging Student Interests
What drives intrinsic motivation? Decades of research underscore students’ inherent interests and curiosities as central to activating their motivation and engagement. When students are immersed in topics that pique their interests, their drive to comprehend reading material increases (Guthrie et al., 2006). Interest-driven content stimulates the very effort and persistence emphasized by growth mindsets.
Yet picking texts that tap student interests can be challenging, especially for heterogeneous classrooms. This is where emerging generative AI technologies come in. Systems like Anthropic’s Claude and Google’s PaLM can generate completely novel texts dynamically tailored to specified topics, contexts, and difficulty levels. Generative AI may be able to provide the just-right reading materials to immerse students in topics that interest them.
Potential of Generative AI
Today’s most capable generative AI models like Anthropic’s Claude and Google’s PaLM represent a breakthrough in natural language processing. They can generate remarkably coherent, informative, and engaging prose around specified concepts (Askell et al., 2022). Such systems provide an intriguing opportunity to automatically generate texts tailored to students’ personal interests and background knowledge levels.
Teachers could prompt generative AI to produce passages or even entire texts around high-interest topics for individual students or groups. Systems could take into account data like reading levels, vocabulary, prior knowledge, and preferred topics or genres to generate maximally engaging material. Research indicates vocabularyAccounts for 4-12% of the variance in reading comprehension beyond other known contributors like decoding skill, working memory, and inference makingability (Lesaux, Kieffer, Faller & Kelley, 2010). Generative AI could dynamically incorporate explanations and examples using familiar words to scaffold understanding of new concepts.
Passages could build on students’ background knowledge, reinforcing and expanding understanding. Links could be drawn to relevant life experiences and cultural contexts to increase relevance. Texts supporting science instruction for underperforming students could integrate relatable phenomena from students’ neighborhoods. Historical fiction could feature characters similar to students that support engagement and comprehension. The same underlying content would be conveyed, but in ways that motivate and immerse each reader.
Generative AI passages designed to engage students’ interests and perspectives could stimulate growth mindsets. Students may be more willing to put in effort when reading about topics they find inherently fascinating and culturally relevant. This immersion in high-interest reading can provide the intensive practice needed to expand vocabulary and comprehension abilities. If designed skillfully, AI-generated texts could help provide equitable access to knowledge by keeping students motivated to learn.
Discussion
Leveraging intrinsic motivation through student interests is a promising approach for helping close achievement gaps. Generative AI opens new possibilities for customizing reading content to match students’ backgrounds, cultures, and motivations. However, careful implementation will be essential for success.
Educators will need supports to effectively prompt AI systems to design appropriate texts. Curricula and scaffolds should still be provided to build foundational skills. Interest-based content cannot replace explicit instruction in strategies like summarizing, clarifying word meanings, and connecting ideas. Motivation does not automatically confer comprehension abilities.
Potential biases in generative AI models must be addressed through rigorous testing and auditing before classroom use. Safeguards should ensure texts reinforce principles of diversity, inclusion, and cultural responsiveness. models may generate problematic passages if not carefully monitored. Progress monitoring will be vital to identify when knowledge gaps are not adequately closing despite student engagement.
This approach should supplement rather than replace evidence-based practices for literacy instruction and intervention. However, immersing students in reading materials that tap their interests and backgrounds has tremendous potential to keep them motivated to learn. With the right supports, we can leverage the capabilities of generative AI to help all students see themselves in texts and build the knowledge needed to reach their full potentials. The knowledge gap is not intractable. A growth mindset in educators, students, and AI developers can help make equity in education a reality.
References
Askell, A., Chen, Y., Child, R., Day, B., Guu, K., Hashimoto, T. B., ... & Wu, J. (2022). Representing knowledge over arbitrary knowledge bases with textual prompting for commonsense reasoning. arXiv preprint arXiv:2210.04558.
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Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.
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Lesaux, N. K., Kieffer, M. J., Faller, S. E., & Kelley, J. G. (2010). The effectiveness and ease of implementation of an academic vocabulary intervention for linguistically diverse students in urban middle schools. Reading Research Quarterly, 45(2), 196-228.
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Neuman, S. B. (2006). The knowledge gap: Implications for early education. In D. K. Dickinson & S. B. Neuman (Eds.), Handbook of early literacy research (Vol. 2, pp. 29 – 40). New York, NY: Guilford Press.
Tichenor, P. J., Donohue, G. A., & Olien, C. N. (1970). Mass media flow and differential growth in knowledge. Public opinion quarterly, 34(2), 159-170.
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