Course Description

Examination of human–artificial intelligence interaction (AI) in educational assessment, including the use of large language models for assessment development and scoring. Topics include prompt engineering, fine-tuning, human-AI collaboration, human-in-the-loop supervision, and issues of validity, fairness, transparency, and trust in AI-generated outputs.

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Athena Title

Human-AI Interaction

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Semester Course Offered

Offered spring

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Grading System

A - F (Traditional)

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Student learning Outcomes

• Students will explain the principles of human-AI interactions.
• Students will describe the advantages and limitations of large-language models for educational assessment.
• Students will compare automation and augmentation in human-AI collaboration.
• Students will iteratively develop prompts and fine-tune pre-trained models for educational assessment tasks.
• Students will identify and mitigate bias in LLM-generated outputs.
• Students will evaluate the reliability, validity, and fairness of AI-assisted assessment tasks.
• Students will design and conduct a research study applying human-AI interaction principles.

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Topical Outline

• Introduction to Human-AI interaction
• Transformers and large-language models (LLMs)
• Prompt engineering (zero-shot, few-shot, chain-of-thought, prompt chaining)
• Fine-tuning approaches
• Reliability and consistency of LLM output and human-in-the-loop validation
• Algorithmic fairness
• Automatic item generation, scoring, and feedback

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Syllabus

Select Faculty / Instructor to download syllabus:   Gorgun, Guher

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Public CV

Select a file to download:   Gorgun, Guher

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