GoMyCode
Applied AI Engineer intern
Applied AI Engineer Intern (PFE)
Topic: Evaluation and Optimization of AI-Powered Automated Grading Systems
Project applied to: GOMYTEACHER
Project Overview
With the increasing use of AI in educational assessment automated grading systems must ensure accuracy fairness and consistency across subjects and learner levels.
This internship aims to scientifically evaluate and improve the reliability of AI-powered grading systems used in GOMYTEACHER.
Main Objectives
- Compare AI-generated grades with human instructor evaluations
- Analyze grading performance across different subjects:
Programming (Python / JavaScript)
Data Science
Cybersecurity
Math & logic
Theoretical vs practical assessments
GPT-4 / GPT-4o
Claude
Open-source models (Llama Mixtral)
- Evaluate grading dimensions:
Score accuracy
Feedback relevance
Consistency
Bias & variance
Hallucination rate
- Propose improvement strategies:
Prompt engineering
Rubric-based grading
Hybrid grading (AI rules human-in-the-loop)
Methodology
- Build a gold-standard dataset using real student submissions graded by multiple instructors
- Design an AI grading pipeline (grade feedback confidence score)
- Run multi-LLM benchmarking experiments
- Perform quantitative evaluation (MAE Cohens Kappa correlation variance)
- Conduct qualitative error and feedback analysis
Expected Deliverables
Technical
- AI grading evaluation framework
- LLM benchmarking dashboard
- Subject-wise performance reports
Academic
- Final PFE report
- Evaluation tables & graphs
- Reproducible experiments
Business Impact
- Decision matrix: best LLM per subject
- Human validation thresholds
- Cost vs performance trade-offs
Optional Extensions
- Bias & fairness analysis
- Confidence-aware grading with human review flags
- Self-improving grader using instructor feedback
How to apply
Please send your resume to
Required Experience:
Intern
GoMyCode Applied AI Engineer intern 85 Application(s) Applied AI Engineer Intern (PFE)Topic: Evaluation and Optimization of AI-Powered Automated Grading Sy...
GoMyCode
Applied AI Engineer intern
Applied AI Engineer Intern (PFE)
Topic: Evaluation and Optimization of AI-Powered Automated Grading Systems
Project applied to: GOMYTEACHER
Project Overview
With the increasing use of AI in educational assessment automated grading systems must ensure accuracy fairness and consistency across subjects and learner levels.
This internship aims to scientifically evaluate and improve the reliability of AI-powered grading systems used in GOMYTEACHER.
Main Objectives
- Compare AI-generated grades with human instructor evaluations
- Analyze grading performance across different subjects:
Programming (Python / JavaScript)
Data Science
Cybersecurity
Math & logic
Theoretical vs practical assessments
GPT-4 / GPT-4o
Claude
Open-source models (Llama Mixtral)
- Evaluate grading dimensions:
Score accuracy
Feedback relevance
Consistency
Bias & variance
Hallucination rate
- Propose improvement strategies:
Prompt engineering
Rubric-based grading
Hybrid grading (AI rules human-in-the-loop)
Methodology
- Build a gold-standard dataset using real student submissions graded by multiple instructors
- Design an AI grading pipeline (grade feedback confidence score)
- Run multi-LLM benchmarking experiments
- Perform quantitative evaluation (MAE Cohens Kappa correlation variance)
- Conduct qualitative error and feedback analysis
Expected Deliverables
Technical
- AI grading evaluation framework
- LLM benchmarking dashboard
- Subject-wise performance reports
Academic
- Final PFE report
- Evaluation tables & graphs
- Reproducible experiments
Business Impact
- Decision matrix: best LLM per subject
- Human validation thresholds
- Cost vs performance trade-offs
Optional Extensions
- Bias & fairness analysis
- Confidence-aware grading with human review flags
- Self-improving grader using instructor feedback
How to apply
Please send your resume to
Required Experience:
Intern
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