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AI-Homework: Concept Document & Primary Business Requirements

1. Purpose

To provide students with an AI-driven, tutor-style system that helps them understand and complete homework through guided conversation, without enabling cheating, and to provide instructors with an adaptive, controllable, and transparent system that integrates with existing homework platforms.

The objective is to transform homework from answer production into skill acquisition, using AI as a learning partner.

2. High-Level Vision

AI-Homework is an AI-powered tutoring system designed specifically to assist students with homework assignments across various disciplines. Unlike traditional AI homework solvers that simply provide answers, AI-Homework focuses on teaching students how to solve problems themselves through a guided, conversational approach.

For students, AI-Homework:

  • guides students step-by-step,
  • asks conceptual questions,
  • gives personalized hints,
  • adapts difficulty,
  • and prevents direct answer dumping until appropriate.

For instructors, it offers:

  • Teach your-way settings with tight control over what AI can reveal,
  • insights into student understanding,
  • integration with LMS systems,
  • easy content import & authoring tools.

This is not an “AI that does homework.”
It is an “AI that teaches students how to do homework.”

Core Values

AI-Homework is built around the following foundational values:

  • Socratic first
    The system prioritizes questions, prompts, and guided reasoning before giving direct explanations.

  • Learning-first
    Every feature is designed to improve student understanding, retention, and mastery, not provide shortcuts.

  • Guardrail by design
    The platform includes built-in constraints that ensure academic integrity, prevent unproductive answer dumping, and align AI behavior with instructor expectations.

  • Conversational scaffolding
    AI supports students through structured dialogue, prompting next steps, identifying misconceptions, and adapting to student progress.

  • Skill tagging
    Every problem, interaction, and explanation is linked to explicit skill tags, enabling rich analytics and targeted remediation.

  • Clean, intuitive UI
    The interface minimizes cognitive load, supports accessibility, and keeps focus on the learning experience.

  • Strong analytics
    Insightful, instructor-facing analytics reveal performance patterns, common misconceptions, and mastery trajectories across individuals and cohorts.

  • Instructor amplification
    The system enhances, extends, and supports instructor capabilities—helping teachers do the deeper pedagogical work they value but rarely have time for. AI-Homework works with instructors, never instead of them.

  • Instructor-load neutrality
    Features are designed to improve teaching quality, not to increase class sizes or replace human labor. The platform avoids designs that encourage scaling beyond healthy pedagogical limits.

  • Human connection priority
    AI-Homework supports the relational aspects of teaching by freeing instructors from routine tasks so they can focus on mentorship, discussion, creativity, and interpersonal engagement.

  • Cross-discipline
    The platform supports diverse academic fields—including quantitative, qualitative, and code-based disciplines—to enable institution-wide adoption.

3. Stakeholders

3.1 Primary stakeholders

  • Students
    Want guided help, practice, and explanation without punishment or confusion.

  • Instructors
    Want academic integrity, reinforcement of learning, and protection against AI misuse.

3.2 Secondary stakeholders

  • Academic departments
  • LMS administrators
  • University tutoring centers
  • Publishers & online course providers

4. Core Features Overview

AI-Homework will consist of five pillars:

  1. Homework Content Interpreter
  2. Adaptive AI Tutor Engine
  3. Instructor Dashboard & Control Center
  4. Student Learning Interface
  5. Analytics & Insights

Each pillar includes specific business requirements.

5. Detailed Business Requirements

5.1 Pillar 1: Content Ingestion & Generation (Cross-Discipline)

A system for ingesting, understanding, structuring, and generatively extending homework problems, in a way that unifies “homework” and “tutor conversation” into a single object.

5.1.1 Unified Problem Representation

Goal: Treat each homework item as a conversation-ready learning object, not just a static question.

Each problem is stored internally in a normalized format, for example:

  • Problem stem (student-facing text)
  • Target skills / concepts (e.g., “CLT”, “two-sample t-test”, “chain rule”)
  • Solution outline (step-by-step reasoning, not just the final answer)
  • Final answer (or rubric)
  • Expected intermediate steps (for guided conversation)
  • Common misconceptions / wrong paths
  • Metadata: discipline, topic, difficulty, prerequisites
  • Tutor script hints: suggested questions, analogies, scaffolding levels

This representation is used by:

  • The tutor engine (to drive the conversation)
  • The variation generator
  • The analytics layer (to tag skills and errors)

5.1.2 Source 1: Import Existing Homework (WeBWorK, Canvas, etc.)

Use case: Instructor already has homework in WeBWorK, Canvas, TopHat, PDFs, etc., and wants AI-Homework to “wrap” it with tutoring.

Requirements:

  1. Format adapters
    • Adapters for:
      • WeBWorK PG/PGML
      • LMS exports (QTI, Canvas question banks)
      • Simple CSV / JSON question sets
      • Plain text / Markdown
    • Each adapter converts source format into the unified problem representation.
  2. Generative reconstruction
    • When imported content is underspecified (e.g., just a stem and answer), the system uses generative AI to:
      • Propose a solution outline.
      • Propose intermediate steps.
      • Propose hint candidates.
      • Suggest concept tags.
    • Instructor can review/edit these before publishing.
  3. Bulk import workflow
    • Instructors can upload an entire set (e.g., a WeBWorK set) and get:
      • A list of reconstructed problems.
      • Auto-generated solution outlines and hints.
      • A simple interface to approve / modify / disable the AI-generated pieces.

5.1.3 Source 2: Create Variations on Existing Homework

Use case: Instructor wants multiple versions of a problem or wants to reduce sharing/copy-paste cheating while preserving learning goals.

Requirements:

  1. Parametric variation
    • For structured problems (like WeBWorK with randomized parameters), AI-Homework:
      • Reads the parameter ranges.
      • Generates new parameter sets.
      • Ensures solutions remain valid.
    • This is mostly deterministic / symbolic rather than LLM-based.
  2. Structural variation (AI-based)
    • For non-parametric problems, the system can generatively produce:
      • Different numeric values.
      • Different contexts (coffee/soda vs. cats/dogs).
      • Rephrasings of the stem.
      • Alternate but equivalent forms (e.g., algebraically equivalent expressions).
    • It must:
      • Preserve the same concept tags.
      • Recompute or verify the solution outline and final answer.
  3. Difficulty scaling
    • From a single base problem, generate:
      • An easier version (more guided, simpler numbers).
      • A harder version (less guided, more abstraction).
    • Instructor can approve which variants are used in which assignment.

5.1.4 Source 3: Generate Homework from Textbook or Lecture Snippets

Use case: Instructor highlights a section of text or uploads a slide, and wants the system to generate appropriate questions.

Requirements:

  1. Input types
    • Text snippets (copy-paste from textbook or notes).
    • Uploaded PDFs with highlighted regions.
    • Bullet-point lecture summaries.
  2. Question generation
    • System proposes:
      • Concept-check questions (“What is the definition of…?”).
      • Application questions (calculate / derive / prove).
      • Interpretation questions (explain the result, compare two ideas).
    • Each generated question comes with:
      • A proposed solution outline.
      • Skill tags.
      • Difficulty tag.
      • Example hints.
  3. Domain-agnostic handling
    • For quantitative disciplines (math, stats, physics, CS, econ):
      • Emphasis on numeric and symbolic problems.
    • For qualitative disciplines (history, philosophy, writing):
      • Emphasis on analytical, comparative, and argument-structure questions.
    • For coding disciplines:
      • Emphasis on code-writing, debugging, and reading exercises.
  4. Instructor control
    • Instructor can:
      • Accept/reject/edit questions.
      • Mark certain generated questions as “practice only” or “graded”.
      • Save question templates for reuse in future semesters.

5.1.5 Homework-as-Conversation Design

Goal: Homework items are constructed from the beginning as objects that drive a conversation, not static one-shot questions.

Requirements:

  1. Conversation templates
    • Each problem can include a “tutor script”:
      • Suggested opening question from AI.
      • Key checkpoints (e.g., “Now compute the standard error…”).
      • Known likely misconceptions and how the AI should respond.
    • These can be generated by AI and then edited by the instructor.
  2. Stateful structure
    • The unified representation must support:
      • Student state (what they have attempted, which hints used).
      • Branching paths (e.g., if the student confuses variance with standard deviation, follow branch B).
    • This enables the same object to power:
      • The chat interface.
      • Analytics on problem steps.
      • Replays for review.
  3. Cross-discipline extensibility
    • The representation should be generic enough to support:
      • Numerical problems.
      • Symbolic derivations.
      • Code-writing questions.
      • Short-answer conceptual questions.
      • Essay prompts (with rubric-based support).

5.1.6 Guardrails for Generative Use

Because generative tools are used heavily in this pillar, we add explicit guardrails:

  1. Instructor-in-the-loop
    • No AI-generated question, solution, or hint becomes “live” without instructor approval (at least in early versions).
  2. Versioning
    • System keeps versions of problems so instructors can revert AI edits.
  3. Traceability
    • Mark which parts were AI-generated vs. instructor-written.
  4. Quality checks
    • Basic automated checks:
      • Does the solution match the problem?
      • Are units consistent?
      • Are multiple-choice options plausible and unique?

5.1.7 Support for Long-Form Student Answers (AI Reading, Feedback, and Grading)

AI-Homework must support homework items where students submit extended responses rather than numeric or multiple-choice answers. This includes:

  • Written explanations and justifications
  • Proofs (math/stat/CS theory)
  • Data interpretation paragraphs
  • Short essays or constructed responses
  • Coding exercises with textual reasoning
  • Lab reports or structured writeups

This requires the system to handle both generation of such questions and evaluation of student responses.

Requirements for Long-Form Response Support
  1. Flexible Answer Input
    • Student must be able to submit:
      • Plain text
      • Formatted math (LaTeX or MathQuill)
      • Code blocks (Python, R, C++, etc.)
      • Multi-paragraph prose
      • Uploaded images or PDF snippets for handwritten work
  2. AI Reading & Understanding
    • The system must be able to:
      • Parse the student’s argument
      • Identify the main claim, supporting reasoning, and conclusion
      • Detect mathematical/logical errors
      • Recognize correct but non-standard solution paths
      • Distinguish between reasoning errors and algebraic slips
  3. Structured Feedback Generation
    • The feedback must be:
      • Specific to the student’s reasoning
      • Highlight strengths (“Your setup of the null hypothesis is correct”)
      • Flag issues (“However, the pooled variance formula was applied incorrectly”)
      • Provide targeted suggestions (“Try recomputing the standard error using…”)
    • The system must avoid:
      • Overly generic comments
      • Providing full solutions unless instructor allows
  4. Rubric-Based Scoring
    • Instructors can define scoring rubrics with criteria such as:
      • Accuracy
      • Completeness
      • Logical flow
      • Justification quality
      • Use of terminology
      • Coding style or output validity
    • AI produces:
      • A per-criterion score
      • An overall score
      • Justification for each score
  5. Instructor Override & Review
    • Instructors must be able to:
      • Edit rubric criteria
      • Review and override AI’s feedback or score
      • Provide additional comments
      • Export graded responses back to LMS
  6. Bias & Consistency Controls
    • AI grading must:
      • Maintain consistent standards across submissions
      • Avoid demographic or stylistic bias
      • Provide anonymized scoring options
    • Optionally:
      • Double scoring mode, where two independent AI evaluations are compared
      • Automatic flagging of uncertain evaluations for human review
  7. Learning-Focused Feedback Mode
    • For formative (non-graded) assignments:
      • AI provides stepwise revision guidance, such as:
        • “Rewrite your conclusion to address the original question more directly”
        • “Your first derivative calculation is correct, now check the sign of f’‘(x)”
      • Students can iterate and resubmit for improved understanding
  8. Discipline-Specific Reasoning Models
    • Stats/math:
      • Emphasis on logic, justification, and step structure
    • Humanities:
      • Emphasis on argumentation, evidence, coherence
    • CS:
      • Emphasis on code reasoning + correctness + readability
  9. Explainability
    • Students must be able to ask:
      • “Why did I lose points here?”
      • “Can you explain the mistake in paragraph 2?”
      • “What is a better way to structure my argument?”
    • AI must be able to answer using the rubric and reference model solutions.
Why This Feature Is Essential
  • Allows you to target every discipline, not just quantitative fields.
  • Lets instructors assign deeper, conceptual homework while still controlling AI misuse.
  • Supports writing-heavy STEM fields (bio, psych, social science, CS design questions).
  • Gives the system a huge advantage over existing homework platforms (which cannot grade long-form reliably).

5.2 Pillar 2: Adaptive AI Tutor Engine

The core learning logic.

Requirements

  1. Socratic tutoring
    • System must nudge the student toward the next logical step.
    • Must ask questions (“What does this variable represent?”).
    • Must offer partial guidance without giving the full solution immediately.
  2. Multi-mode scaffolding
    • Hint mode: small nudges.
    • Step-by-step guidance: students must attempt each step.
    • Conceptual explanation mode: explain underlying theory.
    • Practice generation: generate similar-but-different problems.
  3. Anti-cheating guardrails
    • Never reveal full solutions until student demonstrates sufficient attempts or instructor permits.
    • Instructor-configurable “strict” and “lenient” modes.
    • Ability to detect “give me the answer” behavior and gently redirect.
  4. Personalization
    • Track student weak areas.
    • Adjust hint style (visual, numeric, conceptual).
    • Provide extra practice targeted to weaknesses.
  5. Explainability
    • Explanations must be correct, structured, and math-safe.
    • Tutor must show reasoning clearly and avoid hallucinations via templates.

5.3 Pillar 3: Instructor Dashboard & Control Center

Requirements

  1. Assignment configuration
    • Set allowable hint levels.
    • Allow or disallow solution reveal.
    • Configure requirement for student attempts before revealing steps.
  2. Content management
    • Upload PG/PGML sets.
    • Tag problems by learning objective.
    • Preview how AI will tutor students.
  3. Class analytics
    • Identify topics most students struggle with.
    • View aggregated (but respectful) data on common errors.
    • Track time spent per problem.
  4. Integrity controls
    • Toggle strict “tutor only, never answer” mode.
    • Option to disable AI during timed assessments.
    • Export logs for academic review.
  5. Integration & exports
    • Export grade suggestions back to LMS.
    • Sync with Canvas/Blackboard via LTI.

5.4 Pillar 4: Student Learning Interface

Requirements

  1. Conversation interface
    • Chat-based interface with math rendering.
    • Students can upload screenshots of problems.
    • History saved per problem.
  2. Attempt tracking
    • Students attempt steps manually.
    • System acknowledges progress.
    • Must keep track of how many hints were used.
  3. Adaptive help
    • Students can request:
      • “Explain another way”
      • “Easier example”
      • “Show me how you would start”
      • “Ask me questions instead of explaining”
  4. Motivational design
    • Progress meters.
    • Concept mastery indicators.
    • “Review this later” bookmarks.
  5. Accessibility
    • Full keyboard accessibility.
    • Dyslexia-friendly fonts and settings.
    • Screen reader support.

5.5 Pillar 5: Analytics & Insights

Requirements

  1. Student analytics
    • Mastery score per topic.
    • Summary of hint usage.
    • Common mistake patterns.
  2. Instructor analytics
    • Problem difficulty calibration.
    • Topic-level heatmaps.
    • Evidence of learning improvement over time.
  3. Privacy
    • Must comply with FERPA.
    • Must allow anonymized data sharing for research.
    • Must allow student deletion requests.

5.x Pedagogical Modes and Configuration

AI-Homework must support a wide range of teaching philosophies and homework workflows. Instructors, departments, and institutions have different expectations about how AI should support student learning, and the system must provide configurable pedagogical modes rather than enforce a single instructional vision.

The goal of this pillar is to empower instructors to shape how the AI behaves, what help is permitted, and how students interact with homework, creating a platform that adapts to diverse educational contexts.

5.x.1 Overview of Pedagogical Flexibility

AI-Homework provides a configurable framework that governs:

  • How much assistance the AI may provide
  • What form that assistance takes (hints, steps, full reasoning, etc.)
  • When help becomes available
  • What constitutes a valid attempt
  • How the AI evaluates, critiques, and grades work
  • How solutions are revealed or withheld
  • How feedback is structured (Socratic vs. direct)
  • How mastery is measured and reported

This flexibility supports instructors across disciplines ranging from statistics, mathematics, physics, and CS to writing-intensive humanities and social sciences.

5.x.2 Pedagogical Configuration Dimensions

Each course, assignment, or individual problem can specify settings along a set of pedagogical dimensions. Key dimensions include:

  1. Help Availability
    • Immediate help (AI responds on first request)
    • Attempt-gated help (1, 2, or n attempts required)
    • Strict mode (no help until instructor-declared checkpoints)
    • Help disabled entirely for exams or high-stakes assessments
  2. Hint Style
    • Conceptual hints (definitions, clarifications)
    • Directional hints (what to think about next)
    • Procedural hints (steps or formulas)
    • Partial solution steps
    • Fully worked solutions (if permitted)
  3. Solution Visibility
    • Never reveal solution
    • Reveal only after mastery
    • Reveal only after submission deadlines
    • Reveal after sufficient attempts
    • Reveal upon instructor override
  4. Feedback Style
    • Minimal feedback (confirm/deny correctness)
    • Socratic questioning (guided discovery)
    • Explanation mode (clear reasoning and justification)
    • Diagnostic mode (error detection and misconception tagging)
    • Revision mode (step-by-step improvement guidance)
  5. Generative Creativity
    • Disable generative variation entirely
    • Only generate variants with instructor approval
    • Allow AI-generated practice problems (ungraded)
    • Allow dynamic generation based on student performance
    • Permit instructors to author generative templates
  6. Academic Integrity Level
    • High integrity: no solutions, no shortcuts
    • Balanced: hints allowed, controlled solution reveal
    • Growth-focused: solutions allowed for learning, not grading
    • Open learning: students can freely explore alternate solution paths
  7. Mastery & Assessment
    • Attempt-based mastery
    • Step-correctness mastery
    • Rubric-based mastery for long-form work
    • Concept-tag mastery (skill-tag proficiency tracking)
    • AI-detected mastery (behavioral + performance signals)

These dimensions combine multiplicatively to create a wide range of pedagogical modes.

5.x.3 Predefined Pedagogical Modes (Instructor Selectable)

AI-Homework includes several preset modes for quick adoption, each mapping into configurations across the dimensions above.

  1. Traditional Tutor Mode
    • Socratic questions only
    • No solutions revealed
    • Students must justify steps
  2. Hint-First Mode
    • Immediate access to conceptual and directional hints
    • No procedural steps until attempts made
    • Solutions optional after completion
  3. Scaffolding Mode
    • Step-by-step guidance
    • Procedural hints permitted early
    • Adaptive difficulty adjustments
  4. Instructor-as-Coach Mode
    • AI acts as an assistant instructor:
      • Explains concepts
      • Provides alternative solution paths
      • Generates practice problems
    • Solutions allowed with justification
  5. Autograder Mode
    • Strict rubric-based evaluation
    • Feedback tied directly to rubric criteria
    • Minimal guidance during initial attempt
    • Long-form and code-aware grading supported
  6. Open Exploratory Mode
    • Students engage in extended dialogue
    • AI may explain, expand, or follow student curiosity
    • Suitable for humanities, conceptual discussions, or project-based courses
  7. Exam Integrity Mode
    • AI assistance fully disabled
    • Only autograding permitted
    • No hints or solution visibility
    • Secure session controls

Instructors can start with a preset, then customize further.

5.x.4 Custom AI Behavior Profiles

Instructors and departments may define custom pedagogical profiles, specifying:

  • Help rules
  • Solution reveal policies
  • Feedback tone
  • Allowed hint types
  • Integrity-level thresholds
  • Rubric templates
  • Discipline-specific language styles

Example:

Dr. Singh’s Proof Writing Profile

  • Hints allowed only after 2 attempts
  • Socratic questioning primary
  • No procedural hints (e.g., “subtract this from both sides”)
  • Focus feedback on logical flow
  • Provide alternate proof strategies only after completion

Profiles can be exported, shared, imported, and versioned.

5.x.5 Course-, Assignment-, and Problem-Level Overrides

Configuration can be applied at:

  • Course Level — broad teaching philosophy
  • Assignment Level — e.g., formative vs. summative
  • Problem Level — granular control for specific tasks

Example:

  • Course uses Scaffolding Mode
  • Assignment 3 (hypothesis testing) uses strict solution visibility
  • Problem 5 specifically allows procedural hints due to common student struggles

This hierarchical system allows fine-grained control without overwhelming instructors.

5.x.6 Student Personalization within Instructor Boundaries

While instructors define the outer boundaries, students may tailor:

  • Feedback detailedness
  • Preferred hint styles
  • Requesting additional examples
  • Asking for alternative explanations
  • Accessibility preferences

Personalization never exceeds instructor limits.

5.x.7 Rationale for Pedagogical Flexibility

This configuration system is essential for adoption because:

  • Instructors differ widely in views about AI in education
  • Departments may have compliance or integrity rules
  • Different disciplines require different interaction styles
  • Instructors need control to trust an AI system
  • A one-size-fits-all tutoring model is unacceptable in higher education

Pedagogical configurability transforms AI-Homework into a platform, not a prescriptive tool.

6. Non-Functional Requirements

6.1 Reliability

  • Uptime 99.5%+ during assignment windows.
  • Graceful fallback if AI temporarily fails.

6.2 Performance

  • Tutor response time < 2 seconds for 95th percentile.
  • Imports of typical WeBWorK sets (<50 questions) processed in < 15 seconds.

6.3 Scalability

  • Serve multiple courses across universities.
  • Support large 200–500 student classes.

6.4 Security

  • FERPA-compliant data storage.
  • OAuth integration with university SSO.
  • Strict role-based permissions.

7. Competitive Advantages

  1. Not an AI solver, but an AI teacher.
  2. Integrates with actual university homework systems.
  3. Understands PG/PGML, something no other AI tutor currently does.
  4. Instructor-controlled guardrails.
  5. Adaptable to any STEM language (math, stats, CS, physics, econ).
  6. Personalized, data-driven learning.

8. Next Steps

If you want, we can now produce:

  • A name brainstorming list
  • A system architecture diagram
  • A roadmap (Year 1–5)
  • A technical spec for the PG/PGML interpreter
  • A pitch deck for faculty
  • A business model (pricing tiers + strategy)
  • A risk analysis
  • A prototype design outline
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