Intelligent digital textbook framework with LLM-powered structured dialogue for reading comprehension support¶

Design · All designs
Evidence · no claims cited

Description¶

The learning engineer collaborators are developing a framework for intelligent digital textbooks augmented with AI capabilities. Learners write a summary after reading each page, scored by a finetuned ModernBERT model. If below the passing threshold, a structured dialogue sequence triggers: the learner re-reads a selected passage, then the LLM agent Llama3 generates a self-explanation reading training question, followed by a follow-up question and summary revision advice grounded in the passage and dialogue.

Design Implications¶

Context¶

Requirements¶

Smaller, local LLMs are used to enable lower cost, private deployments with protected student data

Constraints¶

Smaller models often struggle to follow prompt instructions in the same way larger, proprietary models do

Target Learners¶

K-12 and higher education students reading digital textbooks

Learning Goals¶

reading comprehension through self-explanation
summary writing and revision

Claims¶

Examples¶

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Key Sources¶

Adam Coscia, Sujata Duwal, Langdon Holmes, Scott Crossley, and Alex Endert. (2026). Calibrating Trustworthiness: Co-Designing Metrics and Visualizations for Evaluating LLMs in Education. https://arxiv.org/abs/2608.04006

Page metadata

Field	Value
`type`	`design`
`id`	`intelligent-digital-textbook-llm-structured-dialogue`
`title`	`Intelligent digital textbook framework with LLM-powered structured dialogue for reading comprehension support`
`description`	`The learning engineer collaborators are developing a framework for intelligent digital textbooks augmented with AI capabilities.`
`status`	`review`
`generated`	by `process:settle-candidates` on `2026-10-10`
`trust`	unverified — no one has confirmed this page's content
`sources`	1 — `adam-coscia-2026`

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