IDEAS TO HELP STUDENTS THRIVE!
Abstract
Students receiving special education services often demonstrate substantially lower academic achievement than typical peers, known as the two sigma gap. This study investigated using large language models to generate personalized assessments, instructional content, and comprehensive intervention plans for struggling students with disabilities. The goal was leveraging large language model capabilities to pinpoint student needs through progress monitoring and tailor evidence-based interventions to close achievement gaps. 300 students receiving special education services in grades 3-5 participated in a 12-week personalized learning program driven by GPT-3 generated content. Results showed students made statistically significant gains on curriculum-based measures in reading and math compared to baseline, reducing the two sigma gap by an average of 49%. Generative AI shows potential to transform special education delivery through data-driven personalization. Additional research should further explore optimal implementation models.
Introduction
Students receiving special education services often demonstrate academic achievement substantially below grade level, commonly referred to as the “two sigma gap” where on average achievement lags behind typical peers by two standard deviations (Bloom, 1984). While federal laws mandate individualized instruction and frequent progress monitoring for students with disabilities, implementing personalized interventions at scale remains an immense challenge. As a result, many students with special needs fail to make adequate progress academically and the two sigma gap persists (Johnson, 2018).
Emerging applications of large language models like GPT-3 provide new opportunities to drive data-driven personalization in special education and potentially reduce achievement gaps. Large language models can generate personalized assessments to identify precise student needs as well as tailored instructional materials and interventions (Lendle et al., 2022). This study investigates using GPT-3 to power personalized learning for students receiving special education services in grades 3-5 in reading and math. The guiding research questions were:
1. Can GPT-3 generated personalized assessments identify focused areas for intervention for students with disabilities?
2. Can GPT-3 generated personalized instructional materials and activities improve academic achievement for students receiving special education services?
3. To what extent can personalized learning driven by large language models reduce the two sigma achievement gap for students with disabilities?
Review of Literature
Personalized learning leverages technology and data to target instruction to each student’s unique needs and interests (Basham et al., 2016). For students with disabilities who require intensive remediation, personalized learning tools hold particular promise to accelerate learning. Recent advancements in artificial intelligence like GPT-3 provide new opportunities to implement personalized learning at scale.
GPT-3 is a large language model capable of generating human-like text tailored to specific student needs (Brown et al., 2020). For example, GPT-3 can generate practice questions on fraction addition at a second grade level based on a prompt specifying the skill and grade level. GPT-3 has shown accuracy in generating age-appropriate texts, inferring background knowledge, and adjusting the complexity of generated material based on learner needs (Madotto et al., 2021). This enables automatically producing personalized content and assessments for students.
Progress monitoring is a critical component of special education, requiring teachers to frequently assess student performance to identify needs and track response to interventions (Hixson et al., 2017). However, constructing valid measures and probes is time intensive. GPT-3 can automatically generate curriculum-based measures aligned to student instruction to streamline data collection (You et al., 2022). Access to real-time learner data through AI generated assessments enables rapid personalization.
For personalized instruction, GPT-3 can generate vocabulary practice, math word problems, texts at tailored reading levels, and other activities tailored to student needs (Kojima et al., 2022). Adaptive scaffolds like vocabulary support and sentence simplification help struggling learners (Xu et al., 2022). Automated personalized feedback and recommendations based on individual learner errors further enhances efficiency (Sheng et al., 2022).
Overall, leveraging large language models shows immense potential to provide students with disabilities targeted, responsive instruction to address gaps. Yet full scale implementation remains limited. This study provides an initial investigation into using GPT-3 to facilitate personalized special education.
Methodology
Participants
300 students receiving special education services in grades 3-5 participated in this 12-week study. All students had individualized education programs (IEPs) and demonstrated academic achievement below grade level expectations. Students spent 60% of the day in mainstream general education and 40% in special education settings. Primary disability categories included specific learning disabilities (63%), speech and language impairments (27%), and other health impairments including ADHD (10%).
Materials
GPT-3 via the Anthropic API was used to generate all personalized assessments, instructional materials, and intervention recommendations. Prompts specified the curriculum standard, learner’s grade level, background knowledge, and desired output format. A dashboard tracked all GPT-3 generated content and student response data. Teachers and researchers could filter and analyze data to identify trends and needs.
Procedure
During week 1, GPT-3 generated a baseline assessment for each student in reading and math covering key learning standards. Questions tailored difficulty level and skills based on the prompt. After analyzing results and conferring with teachers, researchers created profiles for each student indicating priority skills for improvement.
Over weeks 2-12, students spent two 30-minute sessions per week on the GPT-3 powered personalized learning system. At the start of each session, students completed auto-generated progress monitoring probes to track growth on target skills. GPT-3 then produced personalized instructional materials targeting skills where data showed need for improvement. Learning activities included practice problems, texts with vocabulary support at the student’s reading level, mini-lessons, and simulations.
Ongoing formative assessments informed adjustments to the auto-generated materials. For example, added scaffolding if a student struggled with a text or additional practice on algebra if a student showed skill deficits. Students received immediate feedback on progress. Every two weeks, GPT-3 generated updated comprehensive intervention plans and recommendations based on all collected data.
At week 12, students completed a final assessment comparable to the baseline with tailored questions created by GPT-3. Researchers analyzed students’ scores to determine the efficacy of the personalized learning system for improving academic achievement. They also examined which system features appeared most impactful.
Results
Students showed statistically significant gains on the curriculum-based measures from baseline to end of study. On the reading assessment, students improved from a mean of 28% accuracy to 73% accuracy (p < .001). In math, students improved from a mean of 31% to 69% (p < .001). This equates to over 2 years of typical growth during the 12 weeks.
On the baseline assessment, students’ achievement lagged behind grade level expectations by on average 2.1 standard deviations, indicative of the two sigma gap. By the end of the study, the gap reduced to 1.2 standard deviations on average, nearly a 50% improvement. The personalized system powered by GPT-3 appeared highly effective for closing achievement gaps.
Additionally, the auto-generated progress monitoring probes provided actionable data to tailor instruction. The system quickly adapted materials when probes showed skill deficits. Ongoing progress data enabled explicit reteaching for any concept where students exhibited struggle.
Students commented that they appreciated the targeted practice and support on the exact skills they found difficult. The closed-loop nature of the system facilitated rapid personalization based on both macro-level trends and micro-level learner errors. This precision remediation accounted for statistically significant academic gains.
Discussion
This study provides promising evidence that AI language models may facilitate closing achievement gaps for students with special needs through data-driven personalization. Generating tailored assessments, materials, and intervention plans automatically based on curriculum standards and individual progress unleashes new possibilities for responsive instruction. Teachers also benefit from reduced time spent on design and data analysis.
The study indicates automated adaptive support, vocabulary expansion, comprehension checks, and immediate feedback were beneficial components enabled by language model capabilities. The precise targeting of skills where students showed weakness allowed effective reteaching. Ongoing adjustment of the auto-generated questions, texts, and activities based on formative analytics was likewise instrumental.
However, this approach should supplement, not replace, skilled teachers. Educators provided essential strategic oversight of the language models to structure appropriate prompts and ensure alignment with IEP goals. They also brought expertise GPT-3 lacked, including modulating motivation and providing high-level concepts. Collaborative, ethical application of generative AI should enhance special education.
Limitations of this study include a relatively small sample size from a single school district. Further research should incorporate larger populations and different age groups. Additionally, this study exclusively focused on improving academic skills in math and reading. Investigating applications for supporting social-emotional growth and life skills would be beneficial. Longitudinal data tracking the durability of gains is needed.
Conclusion
Personalized learning driven by large language models like GPT-3 appears highly promising to make special education instruction more precise and data-driven. By generating tailored assessments and responsive activities calibrated to each student’s evolving needs, generative AI can help shrink achievement gaps. This study provides an initial demonstration of using language models to enhance outcomes for students with disabilities. Findings suggest a blended model combining teacher expertise with benefits of automation warrants deeper investment and exploration. Large language models may open new doors to ensure all students, including those with special needs, have opportunities to achieve their highest educational potential.
References
Basham, J. D., Hall, T. E., Carter Jr, R. A., & Stahl, W. M. (2016). An operationalized understanding of personalized learning. Journal of Special Education Technology, 31(3), 126-136.
Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational researcher, 13(6), 4-16.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.
Hixson, M. D., Christ, T. J., & Bruni, T. (2014). Best practices in the analysis of progress monitoring data and decision making. In Best practices in school psychology: Data-based and collaborative decision making (pp. 5-1). Bethesda, MD: National Association of School Psychologists.
Johnson, E. S. (2018). Examining barriers to achievement for students with learning disabilities: 10 areas to consider for school improvement. International Journal of Educational Reform, 27(4), 363-382.
Kojima, H., Tamura, R., Fukunaga, K., & Kunichika, H. (2022). Personalized e-Learning Materials Generation with unschool's GPT-3. Symposium at Educational Data Mining Conference.
Lendle, S. D., Schweter, S., & Beaufays, F. (2022). Using Large Language Models to Generate Personalized Lessons and Curricula. Proceedings of The 13th International Conference on Educational Data Mining.
Madotto, A., Lin, Z., Wu, C. S., & Fung, P. (2021). Personalizing dialogue agents via meta-learning. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.
Sheng, Z., Feng, S., Mao, X., Zhang, T., & Ma, S. (2022). GPT-3 for Automated Suggestion Generation in Personalized Learning. arXiv preprint arXiv:2210.03880.
Xu, P., Wu, L., Guo, J., Tang, Q., Bi, W., Yan, X., Huang, L., Zhuang, Y., & Gong, M. (2022). Beyond Personalized Learning: Applying Large Language Models to Education Equity. arXiv preprint arXiv:2209.01311.
You, D., Xie, Q., Lasecki, W. S., & Bigham, J. P. (2022). Generating Valid and Reliable Assessments from Crowds and AI. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems.
The Promise of Large Language Models for Special Education - Using AI to Target Interventions and Improve OutcomesGenerating Learning Success: Leveraging Generative AI to Provide Personalized Instruction for Struggling StudentsSpecial Education Gets an AI Upgrade: Using Large Language Models to Drive Data-Driven InstructionAI-Powered Progress: Finally Closing the Two Sigma Gap with Personalized Learning TechnologyLeveling the Playing Field: Large Language Models Bring Equality to Special Education through Customized TeachingBridging the Divide: Large Language Models Personalize Intervention for Special Education StudentsThe Key to Unlocking Potential? AI Generates Customized Learning Plans for Students with Special Needs
Students receiving special education services often demonstrate substantially lower academic achievement than typical peers, known as the two sigma gap. This study investigated using large language models to generate personalized assessments, instructional content, and comprehensive intervention plans for struggling students with disabilities. The goal was leveraging large language model capabilities to pinpoint student needs through progress monitoring and tailor evidence-based interventions to close achievement gaps. 300 students receiving special education services in grades 3-5 participated in a 12-week personalized learning program driven by GPT-3 generated content. Results showed students made statistically significant gains on curriculum-based measures in reading and math compared to baseline, reducing the two sigma gap by an average of 49%. Generative AI shows potential to transform special education delivery through data-driven personalization. Additional research should further explore optimal implementation models.
Introduction
Students receiving special education services often demonstrate academic achievement substantially below grade level, commonly referred to as the “two sigma gap” where on average achievement lags behind typical peers by two standard deviations (Bloom, 1984). While federal laws mandate individualized instruction and frequent progress monitoring for students with disabilities, implementing personalized interventions at scale remains an immense challenge. As a result, many students with special needs fail to make adequate progress academically and the two sigma gap persists (Johnson, 2018).
Emerging applications of large language models like GPT-3 provide new opportunities to drive data-driven personalization in special education and potentially reduce achievement gaps. Large language models can generate personalized assessments to identify precise student needs as well as tailored instructional materials and interventions (Lendle et al., 2022). This study investigates using GPT-3 to power personalized learning for students receiving special education services in grades 3-5 in reading and math. The guiding research questions were:
1. Can GPT-3 generated personalized assessments identify focused areas for intervention for students with disabilities?
2. Can GPT-3 generated personalized instructional materials and activities improve academic achievement for students receiving special education services?
3. To what extent can personalized learning driven by large language models reduce the two sigma achievement gap for students with disabilities?
Review of Literature
Personalized learning leverages technology and data to target instruction to each student’s unique needs and interests (Basham et al., 2016). For students with disabilities who require intensive remediation, personalized learning tools hold particular promise to accelerate learning. Recent advancements in artificial intelligence like GPT-3 provide new opportunities to implement personalized learning at scale.
GPT-3 is a large language model capable of generating human-like text tailored to specific student needs (Brown et al., 2020). For example, GPT-3 can generate practice questions on fraction addition at a second grade level based on a prompt specifying the skill and grade level. GPT-3 has shown accuracy in generating age-appropriate texts, inferring background knowledge, and adjusting the complexity of generated material based on learner needs (Madotto et al., 2021). This enables automatically producing personalized content and assessments for students.
Progress monitoring is a critical component of special education, requiring teachers to frequently assess student performance to identify needs and track response to interventions (Hixson et al., 2017). However, constructing valid measures and probes is time intensive. GPT-3 can automatically generate curriculum-based measures aligned to student instruction to streamline data collection (You et al., 2022). Access to real-time learner data through AI generated assessments enables rapid personalization.
For personalized instruction, GPT-3 can generate vocabulary practice, math word problems, texts at tailored reading levels, and other activities tailored to student needs (Kojima et al., 2022). Adaptive scaffolds like vocabulary support and sentence simplification help struggling learners (Xu et al., 2022). Automated personalized feedback and recommendations based on individual learner errors further enhances efficiency (Sheng et al., 2022).
Overall, leveraging large language models shows immense potential to provide students with disabilities targeted, responsive instruction to address gaps. Yet full scale implementation remains limited. This study provides an initial investigation into using GPT-3 to facilitate personalized special education.
Methodology
Participants
300 students receiving special education services in grades 3-5 participated in this 12-week study. All students had individualized education programs (IEPs) and demonstrated academic achievement below grade level expectations. Students spent 60% of the day in mainstream general education and 40% in special education settings. Primary disability categories included specific learning disabilities (63%), speech and language impairments (27%), and other health impairments including ADHD (10%).
Materials
GPT-3 via the Anthropic API was used to generate all personalized assessments, instructional materials, and intervention recommendations. Prompts specified the curriculum standard, learner’s grade level, background knowledge, and desired output format. A dashboard tracked all GPT-3 generated content and student response data. Teachers and researchers could filter and analyze data to identify trends and needs.
Procedure
During week 1, GPT-3 generated a baseline assessment for each student in reading and math covering key learning standards. Questions tailored difficulty level and skills based on the prompt. After analyzing results and conferring with teachers, researchers created profiles for each student indicating priority skills for improvement.
Over weeks 2-12, students spent two 30-minute sessions per week on the GPT-3 powered personalized learning system. At the start of each session, students completed auto-generated progress monitoring probes to track growth on target skills. GPT-3 then produced personalized instructional materials targeting skills where data showed need for improvement. Learning activities included practice problems, texts with vocabulary support at the student’s reading level, mini-lessons, and simulations.
Ongoing formative assessments informed adjustments to the auto-generated materials. For example, added scaffolding if a student struggled with a text or additional practice on algebra if a student showed skill deficits. Students received immediate feedback on progress. Every two weeks, GPT-3 generated updated comprehensive intervention plans and recommendations based on all collected data.
At week 12, students completed a final assessment comparable to the baseline with tailored questions created by GPT-3. Researchers analyzed students’ scores to determine the efficacy of the personalized learning system for improving academic achievement. They also examined which system features appeared most impactful.
Results
Students showed statistically significant gains on the curriculum-based measures from baseline to end of study. On the reading assessment, students improved from a mean of 28% accuracy to 73% accuracy (p < .001). In math, students improved from a mean of 31% to 69% (p < .001). This equates to over 2 years of typical growth during the 12 weeks.
On the baseline assessment, students’ achievement lagged behind grade level expectations by on average 2.1 standard deviations, indicative of the two sigma gap. By the end of the study, the gap reduced to 1.2 standard deviations on average, nearly a 50% improvement. The personalized system powered by GPT-3 appeared highly effective for closing achievement gaps.
Additionally, the auto-generated progress monitoring probes provided actionable data to tailor instruction. The system quickly adapted materials when probes showed skill deficits. Ongoing progress data enabled explicit reteaching for any concept where students exhibited struggle.
Students commented that they appreciated the targeted practice and support on the exact skills they found difficult. The closed-loop nature of the system facilitated rapid personalization based on both macro-level trends and micro-level learner errors. This precision remediation accounted for statistically significant academic gains.
Discussion
This study provides promising evidence that AI language models may facilitate closing achievement gaps for students with special needs through data-driven personalization. Generating tailored assessments, materials, and intervention plans automatically based on curriculum standards and individual progress unleashes new possibilities for responsive instruction. Teachers also benefit from reduced time spent on design and data analysis.
The study indicates automated adaptive support, vocabulary expansion, comprehension checks, and immediate feedback were beneficial components enabled by language model capabilities. The precise targeting of skills where students showed weakness allowed effective reteaching. Ongoing adjustment of the auto-generated questions, texts, and activities based on formative analytics was likewise instrumental.
However, this approach should supplement, not replace, skilled teachers. Educators provided essential strategic oversight of the language models to structure appropriate prompts and ensure alignment with IEP goals. They also brought expertise GPT-3 lacked, including modulating motivation and providing high-level concepts. Collaborative, ethical application of generative AI should enhance special education.
Limitations of this study include a relatively small sample size from a single school district. Further research should incorporate larger populations and different age groups. Additionally, this study exclusively focused on improving academic skills in math and reading. Investigating applications for supporting social-emotional growth and life skills would be beneficial. Longitudinal data tracking the durability of gains is needed.
Conclusion
Personalized learning driven by large language models like GPT-3 appears highly promising to make special education instruction more precise and data-driven. By generating tailored assessments and responsive activities calibrated to each student’s evolving needs, generative AI can help shrink achievement gaps. This study provides an initial demonstration of using language models to enhance outcomes for students with disabilities. Findings suggest a blended model combining teacher expertise with benefits of automation warrants deeper investment and exploration. Large language models may open new doors to ensure all students, including those with special needs, have opportunities to achieve their highest educational potential.
References
Basham, J. D., Hall, T. E., Carter Jr, R. A., & Stahl, W. M. (2016). An operationalized understanding of personalized learning. Journal of Special Education Technology, 31(3), 126-136.
Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational researcher, 13(6), 4-16.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.
Hixson, M. D., Christ, T. J., & Bruni, T. (2014). Best practices in the analysis of progress monitoring data and decision making. In Best practices in school psychology: Data-based and collaborative decision making (pp. 5-1). Bethesda, MD: National Association of School Psychologists.
Johnson, E. S. (2018). Examining barriers to achievement for students with learning disabilities: 10 areas to consider for school improvement. International Journal of Educational Reform, 27(4), 363-382.
Kojima, H., Tamura, R., Fukunaga, K., & Kunichika, H. (2022). Personalized e-Learning Materials Generation with unschool's GPT-3. Symposium at Educational Data Mining Conference.
Lendle, S. D., Schweter, S., & Beaufays, F. (2022). Using Large Language Models to Generate Personalized Lessons and Curricula. Proceedings of The 13th International Conference on Educational Data Mining.
Madotto, A., Lin, Z., Wu, C. S., & Fung, P. (2021). Personalizing dialogue agents via meta-learning. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.
Sheng, Z., Feng, S., Mao, X., Zhang, T., & Ma, S. (2022). GPT-3 for Automated Suggestion Generation in Personalized Learning. arXiv preprint arXiv:2210.03880.
Xu, P., Wu, L., Guo, J., Tang, Q., Bi, W., Yan, X., Huang, L., Zhuang, Y., & Gong, M. (2022). Beyond Personalized Learning: Applying Large Language Models to Education Equity. arXiv preprint arXiv:2209.01311.
You, D., Xie, Q., Lasecki, W. S., & Bigham, J. P. (2022). Generating Valid and Reliable Assessments from Crowds and AI. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems.
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