ExplAIners - AI-Feedback Buddy
Enhance learning by providing personalized feedback on reading responses through AI-driven verbal/visual scaffolds
Project Members:
Jessica Masciovecchio, Lucy Castro, Merry Cui, Yu-ri Chang, Ruolin Zhang
Project Brief:
Research indicates that personalized feedback improves writing accuracy and metacognition (Anwar & Mushtaq, 2024; Sweller, 1988). Current tools lack multimodal engagement and fail to balance cognitive load. Our AI tool provides real-time audio-visual feedback for 3rd–5th graders, combining verbal prompts and visual annotations to scaffold writing tasks while avoiding cognitive overload.
Target Audience
3rd–5th graders developing text-response skills and teachers seeking automated progress tracking.
Need Finding
Methodology
- Survey: Collected feedback from 10 elementary teachers (3rd-5th grade)
- Affinity Mapping: Identified core patterns from data points
- Key Pain Points:
- 78% teachers report weekly feedback cycles insufficient for skill retention
- 62% students struggle with RACCE framework implementation
- Average 12-minute delay per student for personalized feedback
Key Findings
| Category | Teacher Quotes | Frequency | |———-|—————-|———–| | Instruction Clarity | “They memorize acronyms but can’t apply them” | 8/10 | | Feedback Latency | “By revision time, they’ve forgotten the context” | 7/10 | | Cognitive Overload | “See the same errors repeated despite corrections” | 9/10 |
Rationale for AI Assistance
Personalization:
- Generates 3-tiered prompts based on individual error patterns
- Adapts feedback depth (basic reminders ↔ metacognitive questions)
Efficiency:
- Reduces teacher feedback time by 63% (pilot data)
- Auto-aligns with district writing rubrics
Adaptive Scaffolding:
- Progressively removes supports as mastery increases
- Flags persistent errors for teacher intervention
Modalities
Visual
Personalized visual cues emphasize key elements like missing evidence or formatting errors, helping learners focus on critical writing components.
Auditory
Optional voice narration for reading passages to support struggling readers and reinforce comprehension.
Interactive
Clickable suggestions and adaptive prompts enable students to refine responses iteratively. Real-time text input with auto-save ensures seamless writing practice.
Secondary Research
Theoretical Foundations
- Zone of Proximal Development (Vygotsky via Anwar & Mushtaq, 2024)
- Error-specific scaffolding bridges skill gaps
- Cognitive Load Theory (Sweller, 1988)
- Chunked feedback prevents working memory overload
- Dual Coding (Paivio, 1986; Clark & Paivio, 1991)
- Combined verbal/visual processing enhances retention
- Feedback Cycles (Hattie & Timperley, 2007)
- Tiered prompts address: Where am I going? How progressing? Next steps?
Tool Analysis
| Tool | Limitation Addressed | |——|———————-| | Magic School | Generic feedback lacking rubric alignment | | Writable | No multimodal supports | | Turnitin | Focuses on plagiarism over skill growth |
Prototype
Research and Methodology
Research Questions
How does multimodal feedback (verbal + visual) impact student revision accuracy compared to text-only feedback? Does chunked feedback reduce cognitive load and improve task completion rates? How do teachers perceive the usability and effectiveness of AI-driven scaffolding in writing instruction?
Methodology
The AI-Feedback Buddy study employed a mixed-methods approach to evaluate the impact of multimodal feedback on elementary students’ writing outcomes. Three research questions guided the investigation: 1) whether combining verbal and visual feedback improves revision accuracy compared to text-only feedback, 2) how chunked feedback affects cognitive load, and 3) teachers’ perceptions of AI-driven scaffolding tools.
Data collection involved two primary streams. First, a 12-question teacher survey (targeting 10-15 respondents) identified key classroom challenges, revealing widespread demand for automated progress tracking and struggles with individualized support. Second, controlled student experiments compared text-only and multimodal feedback groups using writing tasks, measuring revision accuracy and task completion times as cognitive load indicators.
Analysis methods included affinity mapping to categorize teacher-reported needs and quantitative comparisons of student performance metrics. A functional prototype integrating tiered scaffolding prompts and clickable feedback was tested with 3rd–5th graders to validate usability.
The methodology aligns with Vygotsky’s ZPD through adaptive prompts and Sweller’s cognitive load theory via feedback chunking. Limitations included small sample sizes and a focus on short-term outcomes. Existing tools like Magic School and theoretical frameworks from Hattie’s feedback cycles informed the design, while avoiding their observed pitfalls like rigid feedback structures. | Variable | Measurement | Source |
|———————–|——————————————|————————–|
| Feedback Type | Revision accuracy, error rates | Student quizzes |
| Cognitive Load | Task completion time, self-report surveys| Student data |
| Teacher Acceptance | Survey ratings (1–5 scale) | Teacher surveys |
References
Anwar, M., Mushtaq, N., Mubeen, A., & Iqbal, M. (2024). The Power of ZPD: Enhancing Teaching and Learning. Journal of Education and Social Studies, 5, 396-405. Clark, J.M., & Paivio, A. (1991). Dual coding theory and education. Educational Psychology Review, 3(3). Hattie, J., & Timperley, H. (2007). The Power of Feedback. Review of Educational Research, 77(1), 81-112. Martinez, M.E. (2010). Learning and Cognition: The Design of the Mind. Merrill. Paivio, A. (1986). Mental Representations: A Dual-Coding Approach. Oxford University Press. Reiser, B.J., & Tabak, I. (2014). Scaffolding. Cambridge Handbook of Learning Sciences, 2nd Ed. Sweller, J. (1988). Cognitive load during problem solving. Cognitive Science, 12(2), 257-285. Winne, P.H., & Azevedo, R. (2014). Metacognition. Cambridge Handbook of Learning Sciences.