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Evaluating Machine Expertise: How Graduate Students Develop Frameworks for Assessing GenAI Content
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Evaluating Machine Expertise: How Graduate Students Develop Frameworks for Assessing GenAI Content
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This paper examines how graduate students develop frameworks for evaluating machine-generated expertise in web-based interactions with large language models (LLMs). Through a qualitative study combining surveys, LLM interaction transcripts, and in-depth interviews with 14 graduate students, we identify patterns in how these emerging professionals assess and engage with AI-generated content. Our findings reveal that students construct evaluation frameworks shaped by three main factors: professional identity, verification capabilities, and system navigation experience. Rather than uniformly accepting or rejecting LLM outputs, students protect domains central to their professional identities while delegating others--with managers preserving conceptual work, designers safeguarding creative processes, and programmers maintaining control over core technical expertise. These evaluation frameworks are further influenced by students' ability to verify different types of content and their experience navigating complex systems. This research contributes to web science by highlighting emerging human-genAI interaction patterns and suggesting how platforms might better support users in developing effective frameworks for evaluating machine-generated expertise signals in AI-mediated web environments.
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Cited by 2 Pith papers
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L2-Bench: An Evaluation Benchmark for Measuring LLM Capabilities in Second Language Education
L2-Bench introduces a practitioner-validated taxonomy and 1,000-task rubric benchmark showing frontier LLMs score ~85% on L2 learning-design tasks but drop to ~70% on hard items.
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L2-Bench: An Evaluation Benchmark for Measuring LLM Capabilities in Second Language Education
L2-Bench provides a 1,000-task, expert-validated rubric benchmark showing that frontier LLMs score 50–86% on applied second-language learning-design competencies, with notable weakness on open-ended tasks.
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