Assessment Design in the AI Era: A Method for Identifying Items Functioning Differentially for Humans and Chatbots

Licol Zeinfeld; Alona Strugatski; Ziva Bar-Dov; Ron Blonder; Shelley Rap; Giora Alexandron

cs.HCMar 24, 2026

Assessment Design in the AI Era: A Method for Identifying Items Functioning Differentially for Humans and Chatbots

Authors: Licol Zeinfeld, Alona Strugatski, Ziva Bar-Dov, Ron Blonder, Shelley Rap, Giora Alexandron

Organizations: Weizmann Institute of Science

Abstract

The rapid adoption of large language models (LLMs) in education raises profound challenges for assessment design. To adapt assessments to the presence of LLM-based tools, it is crucial to characterize the strengths and weaknesses of LLMs in a generalizable, valid and reliable manner. However, current LLM evaluations often rely on descriptive statistics derived from benchmarks, and little research applies theory-grounded measurement methods to characterize LLM capabilities relative to human learners in ways that directly support assessment design. Here, by combining educational data mining and psychometric theory, we introduce a statistically principled approach for identifying items on which humans and LLMs show systematic response differences, pinpointing where assessments may be most vulnerable to AI misuse, and which task dimensions make problems particularly easy or difficult for generative AI. The method is based on Differential Item Functioning (DIF) analysis -- traditionally used to detect bias across demographic groups -- together with negative control analysis and item-total correlation discrimination analysis. It is evaluated on responses from human learners and six leading chatbots (ChatGPT-4o & 5.2, Gemini 1.5 & 3 Pro, Claude 3.5 & 4.5 Sonnet) to two instruments: a high school chemistry diagnostic test and a university entrance exam. Subject-matter experts then analyzed DIF-flagged items to characterize task dimensions associated with chatbot over- or under-performance. Results show that DIF-informed analytics provide a robust framework for understanding where LLM and human capabilities diverge, and highlight their value for improving the design of valid, reliable, and fair assessment in the AI era.

Figures & tables

Figure 1: Implemented methodological framework for human–LLM DIF analysis.

	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11	Q12	Q13	Q14	Q15	Q16	Q17	Q18	Q19	Q20	Q21	Q22
Students	0.836	0.531	0.603	0.699	0.921	0.918	0.968	0.968	0.924	0.944	0.740	0.563	0.820	0.827	0.783	0.429	0.484	0.487	0.374	0.552	0.672	0.658
Chatbots	0.979	0.792	0.917	0.781	0.802	0.479	0.667	0.990	1.000	0.979	0.510	0.865	0.979	0.458	0.365	0.917	0.979	0.688	0.802	1.000	0.604	0.240

Table 1: Fraction correct per item for students and chatbots (chemistry).

Figure 2: Human–Chatbot ability distribution.

Inst.	POS	NEG
Psych.	4-6, 9-10, 12, 16, 21-22, 25, 28, 30-33	11, 13-15, 17, 20, 23-24, 34-38, 40
Chem.	5-7, 11, 14-15, 21-22	1-3, 12-13, 16-19

Table 2: MH-DIF: Summary of DIF-flagged items.

Inst.	POS	NEG
Psych.	$\bar{11}$ , $\tilde{13}$ - $\tilde{15}$ , $\tilde{34}$ , $\bar{40}$	$\tilde{16}$ , $\bar{21}$ , $\tilde{25}$ , $\tilde{31}$
Chem.	$\tilde{12}$ , $\bar{16}$ , $\bar{17}$ , $\bar{19}$	$\bar{5}$ - $\bar{7}$ , $\tilde{14}$ , $\bar{15}$ , $\tilde{22}$

Table 3: LR-DIF flagged items:

\bar{Q}

: uniform;

\tilde{Q}

: non-uniform.

Counts	Psych.(MH-DIF)	Chem.(MH-DIF)	Psych.(LR-DIF)	Chem. (LR-DIF)
1	6-7, 10, 12-15, 17, 19-21, 24, 27-28, 33, 38	2-3, 6, 9, 13-14, 17-19, 22	–	14
2	9, 18, 22, 29-32, 37, 39	7, 16	–	6-8
3	2, 8, 16, 26, 34	11, 20, 21	–	–
4	25, 35	–	–	–
5	23, 40	12	–	–
6-7	–	–	–	–

Table 4: Negative Control Analysis: The items on each false_positive_count level (0 omitted).

Educational Assessment

Educational assessment measures what learners know and can do, using tests, rubrics, and learning evidence to guide decisions.

See all 148 papers

Aug 16, 2026

Do Assessment Instruments Measure the Same Thing for Humans and LLMs? A Latent Structure Analysis

The rapid development and growing deployment of large language models (LLMs) have made it increasingly important to understand their capabilities. A common approach is to evaluate LLMs using assessment instruments originally designed to measure skills and competencies in humans, such as standardized exams, and to use performance on these instruments as evidence for generalizable claims about LLMs' underlying abilities on the same skills the assessments are intended to measure in humans. However, from a validity perspective, such inferences require that the relationship between observed performance and underlying constructs established for humans also holds for LLMs. In particular, a necessary condition for transferring score interpretations is similarity in the latent structure of responses to the assessment. In this study, we examine whether this condition holds in two educational contexts: high-school chemistry and a quantitative reasoning section of a university entrance exam. Using a case study design, we compare human response data with responses generated by six multimodal LLMs. Our analytical approach combines exploratory factor analysis, factor congruence, and resampling to assess latent structure similarity across human learners and LLMs. Across both instruments, we find systematic differences between human and LLM factor structures, showing evidence that the analyzed assessments may not measure the same constructs for humans and LLMs. These findings call into question the validity of evaluation practices that use educational assessments to make claims about AI capabilities.

Alona Strugatski, Licol Zeinfeld, Giora Alexandron

Weizmann Institute of Science

LLM Evaluation Psychometric Validation Educational Assessment

Ask PDF

Jun 17, 2026

LLMs Struggle to Measure What Distinguishes Students of Different Proficiency Levels: A Study of Item Discrimination in Reading Comprehension Assessment

Item discrimination is a fundamental psychometric property of educational assessment, which measures whether an item meaningfully distinguishes students with higher proficiency from students with lower proficiency. While various existing works have explored whether large language models (LLMs) can estimate item difficulty, it remains unclear whether they can capture item discrimination. In this work, we evaluate 42 proprietary and open-weight LLMs in zero-shot settings using two complementary approaches: direct discrimination prediction, where models explicitly estimate an item's discrimination value from its content, and response-based Classical Test Theory (CTT) calibration, where LLM answers are treated as synthetic student responses to compute discrimination scores. Our results show that direct prediction yields weak alignment with human-calibrated discrimination: the best-performing model reaches only a Spearman correlation of 0.152. Response-based CTT calibration provides a stronger but still limited signal, with the all-persona synthetic respondent pool reaching a Spearman correlation of 0.241. These findings highlight item discrimination as an open challenge for LLM-based psychometric evaluation: current LLMs contain non-random discrimination-relevant signal, but they do not yet reliably capture how assessment items distinguish human students.

Han Chen, Ming Li, Chenguang Wang +4

MBZUAI · University of Maryland · Virginia Tech

LLM Evaluation Educational Assessment Psychometric Validation

Ask PDF

May 14, 2026

Small, Private Language Models as Teammates for Educational Assessment Design

Generative AI increasingly supports educational design tasks, e.g., through Large Language Models (LLMs), demonstrating the capability to design assessment questions that are aligned with pedagogical frameworks (e.g., Bloom's taxonomy). However, they often rely on subjective or limited evaluation methods; focus primarily on proprietary models; or rarely systematically examine generation, evaluation, or deployment constraints in real educational settings. Meanwhile, Small Language Models (SLMs) have emerged as local alternatives that better address privacy and resource limitations; yet their effectiveness for assessment tasks remains underexplored. To address this gap, we systematically compare LLMs and SLMs for assessment question design; evaluate generation quality across Bloom's taxonomy levels using reproducible, pedagogically grounded metrics; and further assess model-based judging against expert-informed evaluation by analyzing reliability and agreement patterns. Results show that SLMs achieve competitive performance across key pedagogically motivated quality dimensions while enabling local, privacy-sensitive deployment. However, model-based evaluations also exhibit systematic inconsistencies and bias relative to expert ratings. These findings provide evidence to posit language models as bounded assistants in assessment workflows; underscore the necessity of Human-in-the-Loop; and advance the automated educational question generation field by examining quality, reliability, and deployment-aware trade-offs.

Chris Davis Jaldi, Anmol Saini, Shan Zhang +3

Wright State University, USA · University of Florida, USA · TIB – Leibniz Information Centre for Science and Technology, Germany

LLM Evaluation LLM-as-a-Judge Educational Assessment Small Language Models Generative AI in Education

Ask PDF

Abstract

Educational Assessment

Explore similar work

Do Assessment Instruments Measure the Same Thing for Humans and LLMs? A Latent Structure Analysis

LLMs Struggle to Measure What Distinguishes Students of Different Proficiency Levels: A Study of Item Discrimination in Reading Comprehension Assessment

Small, Private Language Models as Teammates for Educational Assessment Design