Fractions are a foundational mathematical concept that many elementary students struggle to
understand (Piaget, 1952). Traditional instruction often relies on memorization and repetition,
which reduces engagement and limits conceptual understanding. The platform introduces an
interactive learning platform that utilizes adaptive difficulty and an AI-powered chatbot to
support students. While artificial intelligence tools are becoming increasingly common in
education, most general systems focus on providing answers rather than ensuring students
understand the reasoning behind them (Burns, 2026). The chatbot encourages students to
explain concepts, promoting understanding instead of answer retrieval (Vygotsky, 1978). The
platform includes instructional units, quizzes, and final assessments to measure learning
outcomes. To evaluate the system’s effectiveness, a study was conducted. It involved students in
grades four through six. Fifteen students tested the platform, with eight using the adaptive
chatbot and seven using the standard chatbot. Results showed that students using the adaptive
chatbot achieved an average of 93.5% on the final assessment, whereas those using the general
model achieved only 83.0%. Results indicate that explanation-based AI tools can strengthen
conceptual understanding in mathematics.
Kriti Regandla, Anika Dubey, Dia Rai et al.· International Journal of Soc...· 0 citations
Personal assistants, chatbots, and large language models (LLMs) increasingly pervade our society. While their main function for people is to answer questions by providing information, a new area of focus and issues was identified in this experiment. There were two main issues identified: namely, that chatbots do not adjust their responses to the user and their background, and that user comprehension is not assessed and responses therefore do not adjust to that comprehension. In this experiment, a self-assessment chatbot was created to attempt to solve this issue. 9 middle school and high school students in the United States were randomly assigned to a control group and an experimental group. Each group was given an instructional document containing information about teen mental health and social media usage. The control group was given Chat GPT to study with and the experimental group was given our self-assessment chatbot. Finally, each group was given the same posttest, scored out of 15 points. This posttest contained 15 questions about the subject material. Results showed statistically significant evidence that students who used the self-assessment chatbot scored, on average, 20.7 percentage points higher than those who used Chat GPT to study, equivalent to about 2 letter grades higher.
Zarah Koroth, Anvi Allada, J. Leddo· International journal of soc...· 0 citations
In previous research, we have shown that teaching students to self-assess and remediate their
own knowledge (gaps) or giving them self-assessment chatbots that take student self-assessments
as inputs and use those inputs in answering students’ questions both lead to large gains in
student performance. The present study compares the relative effectiveness of each approach to
see whether adding a chatbot to aid in remediation provides any benefit to having students selfremediate without the aid of technology. Forty fourth- and fifth-grade students studied a
geometry unit on angle relationships and completed identical instruction followed by a Cognitive
Structure Analysis (CSA)-based self-assessment that identified strengths and deficiencies in four
knowledge categories: facts, strategies, procedures, and rationales. Participants were randomly
assigned to one of two remediation conditions. The control group used its self-assessment to
guide independent review of the instructional materials, whereas the experimental group
submitted the same self-assessment to an LLM-powered chatbot that generated personalized
explanations and guidance targeted to each learner's reported knowledge gaps. Learning was
measured using parallel pretests and posttests. Both groups demonstrated statistically significant
gains from pretest to posttest (both p < .0001). However, students using the self-assessmentinformed chatbot improved by 32.6 percentage points compared with 21.2 percentage points for
students performing self-assessment without chatbot support, a statistically significant difference
of 11.4 percentage points (t(38) = 4.02, p = .0003). These findings suggest that combining
learner-generated knowledge models with LLM-based conversational tutoring produces
substantially greater learning gains than self-directed remediation alone.
Sanjay Rapolu, J. Leddo, Llc MyEdMaster· International Journal of Soc...· 0 citations
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