Detailed Feature Overview
BackflowStudio integrates curriculum design, reflective practice, and longitudinal analytics into a unified local application for instructors. This page expands on the major systems and AI-driven features.
Curriculum Design Framework
BackflowStudio implements a Backward Design workflow that aligns learning outcomes, module objectives, assessments, and instructional assets.
- Course-level outcomes and module-level objectives connected in a single schema.
- Visual mapping of relationships between objectives, assessments, and supporting assets.
- Integration of Bloom’s Taxonomy and Webb’s Depth of Knowledge for cognitive tagging.
- Reusable modules that can be mapped into multiple courses or programs.
- Automatic syllabus generation (HTML/PDF/LMS text).
AI-Assisted Authoring
Integrated AI agents support objective creation, assessment writing, and alignment checks.
| Agent |
Purpose |
Context |
Output |
OutcomeGenerationType |
Generates alternate Course Outcomes and Module Objectives using Backward Design and Bloom’s Taxonomy. |
Title and summary text of the outcome or objective. |
Alternative, aligned text versions. |
AssessmentSuggestionService |
Suggests improved assessment titles and summaries based on module context and mapped objectives. |
Module + Outcomes + Objectives + Cognitive Complexity. |
[AssessmentSuggestion] structured list. |
AssessmentService |
Evaluates cognitive alignment of assessment verbs and intended Bloom level, providing confidence scores. |
Assessment text + Bloom level metadata. |
AssessmentAlignment structured result. |
FeedbackSummaryService |
Summarizes student qualitative feedback, extracts sentiment, and lists positive/negative themes for review reports. |
Student feedback text data. |
FeedbackSummaryResult JSON summary. |
Assessment Reasoning & Alignment
The Assessment Reasoning AI Agent evaluates the pedagogical coherence of assessments in context.
Rather than rewording content, it interprets how well an assessment aligns with its mapped module,
objectives, outcomes, and cognitive complexity. Using natural language reasoning and Bloom’s taxonomy,
it detects mismatches between intent and demand—ensuring that what students are asked to do truly reflects what instructors aim to assess.
- Purpose: Verify the alignment between assessment design, cognitive objectives, and course-level outcomes.
- Analysis: Extracts action verbs and conceptual targets, maps them to Bloom’s levels, and evaluates cognitive trajectory.
- Output: Narrative explanation with confidence score and Bloom-level visualization summarizing where alignment is strong or ambiguous.
- Use Case: Supports instructional design review, course evaluation, and accreditation reporting by identifying pedagogical drift or gaps between objectives and tasks.
Reflective Teaching Tools
Backflow’s Note System lets instructors record insights, corrections, and reminders throughout the semester.
| Note Type |
Description |
Example Use |
general |
Uncategorized or ad-hoc instructor thoughts. |
“Need to revisit this concept before midterm.” |
feedback |
Derived from student comments or in-class sentiment. |
“Students were confused about this example—add visuals next time.” |
review |
For semester-end reflection or program review reports. |
“Revised grading rubric increased clarity.” |
task |
Actionable items surfaced automatically at next course start. |
“Fix typo on slide 15 before next semester.” |
Student Feedback & Annual Review Support
- Import per-term evaluation data (quantitative Likert + qualitative text).
- Display mean scores and response counts for each quantitative item.
- Perform sentiment analysis on textual comments, highlighting per-student polarity.
- AI summarization of feedback themes:
- Top three positive items (praise).
- Top three areas for improvement (needs attention).
- Generate review-ready summaries suitable for inclusion in annual dossiers.
Longitudinal Course Analytics
The Reports page provides multi-term visualization and trend analysis of teaching metrics.
- Cognitive Complexity Trajectory – regression of Bloom-level progression across course timelines.
- Enrollment Trends – per-term student counts with smoothed trendlines.
- Quantitative Feedback Trends – mean evaluation scores with visible raw data distribution.
- Qualitative Sentiment Trends – average sentiment plotted over time with ranked comment list.
- Cross-term analytics connecting teaching design to student outcomes.
Analytical & Reflective Layers
- Trajectory Consistency: Measures change in cognitive complexity slope across terms.
- Assessment Density: Visualizes distribution of Bloom levels per assessment.
- Correlation Analysis: Links cognitive trajectory, sentiment, and enrollment patterns.
- AI Reflective Prompts: Auto-generated reflection questions for instructors.
- Actionable Summary: AI narrative outlining key improvements and trends.
Flexible, Medium-Agnostic Deployment
Backflow links to content wherever it lives—Google Drive, OneDrive, Dropbox, Canvas, or local folders. It never forces file conversion or format preferences.
- Attach any file type (PDF, DOCX, RTF, Markdown, Quarto, video, or dataset).
- Export modules or full course plans as HTML/PDF for distribution.
- Copy formatted content directly into LMS editors such as Canvas or Blackboard.
- “Write once, refine with AI, deploy anywhere.”
Unified Local Design Environment
- macOS, iPadOS, and iOS native document-based architecture.
- Private, local data storage—no external servers required.
- Offline operation with seamless sync through standard file storage.
- Optimized for Apple Silicon performance and responsive design.
← Back to Overview