ELI -Why: Evaluating the Pedagogical Utility of Language Model Explanations (2025)

Abstract: Language models today are widely used in education, yet their ability to tailor responses for learners with varied informational needs and knowledge backgrounds remains under-explored. To this end, we introduce ELI-Why, a benchmark of 13.4K “Why” questions to evaluate the pedagogical capabilities of language models. We then conduct two extensive human studies to assess the utility of language model-generated explanatory answers (explanations) on our benchmark, tailored to three distinct educational grades: elementary, high-school and graduate school. In our first study, human raters assume the role of an “educator” to assess model explanations’ fit to different educational grades. We find that GPT-4-generated explanations match their intended educational background only 50% of the time, compared to 79% for lay human-curated explanations. In our second study, human raters assume the role of a learner to assess if an explanation fits their own informational needs. Across all educational backgrounds, users deemed GPT-4-generated explanations 20% less suited on average to their informational needs, when compared to explanations curated by lay people. Additionally, automated evaluation metrics reveal that explanations generated across different language model families for different informational needs remain indistinguishable in their grade-level, limiting their pedagogical effectiveness.

## introduction language models are increasingly used in education to seek information @xcite , tutoring @xcite , and automated assessment @xcite . a critical aspect of their pedagogical utility is their potential to tailor responses to learners with varying informational needs @xcite @xcite @xcite selective scattering is proportional to the inverse fourth power of wavelength.. figure 1 : distribution of flesch-kincaid reading ease scores for tailored gpt-4-explanations in eli-why: explanations are generated for elementary, high school, and graduate-level backgrounds for "why" questions. interpretations of @xcite ease scores depicting grade-level complexity (@xmath0, region highlighted in pink). @xcite @xcite . this is particularly important in scientific communication, where complex concepts must be conveyed effectively to nonexperts @xcite , and in policy or legal communication, where text must balance technical accuracy with readability @xcite . despite the potential of language models to modify explanations in their complexity @xcite , formality @xcite , and domain specificity @xcite at inference time, it remains unclear whether they can effectively generate responses that are useful both to educators @xcite and to learners alike @xcite . one critical challenge in pedagogy is answering "why" questions. these require explanatory answers to meet different learners where they are. for example, for the question "why is the sky blue?", a high school student might find the explanation "sunlight scatters when it hits air molecules" more understandable, while a physics graduate might find a more technical answer "selective scattering is proportional to the inverse fourth power of wavelength" more satisfactory. although language models are capable of step-by-step reasoning across various tasks @xcite , by default they generate a one-size-fits-all explanation, that might not fit the informational needs of a user interacting with it @xcite . can the prompt-following skills of language models @xcite @xcite help them tailor their explanations foot_0 to users with different informational needs? we introduce eli-why foot_1 , a dataset of 13.4k "why" questions that span different disciplines such as science, medicine, and humanities, such as "why do countries have flags?" or "why do leaves change color in the fall?" to examine the pedagogical utility of language model explanations. while prior studies have explored the ability of language models to generate general-purpose explanations in a pedagogical setting @xcite , it is important that explanations adapt to the prior knowledge of learners @xcite @xcite . our experimental settings involve using the highest educational degree attained as a proxy for the informational needs of a user. specifically, we prompt language models to generate three different explanations to eli-why questions, fit for users with elementary school, high school, or graduate level education. we conduct automated evaluations and two human studies to assess the utility of language model generated grade-tailored explanations. our first human study is conducted from the perspective of an educator to test the appropriateness of an explanation for users with different educational backgrounds on a subset of eli-why. we find that gpt-4-generated explanations match their intended background only 50% of the time, compared to 79% for explanations curated by lay humans ( §4). we then use automated metrics to assess the grade-level readability of explanations; while explanations become more lengthy and contain 'complex' words as the educational level increases, their complexity in terms of grade-level readability often overlaps (shown in figure 1 and section 4.2). we extend this automated metric analysis to three more model families apart from gpt-4, and report similar findings. our second human study tests the appropriateness of an explanation from the perspective of a learner's own selfreported informational needs (section 5). to capture the information needs of users, we asked participants to rate the explanations based on whether they provide new information and whether the explanations connect to their prior knowledge. studies with participants from elementary, high school, and graduate backgrounds (physics and psychology) reveal that gpt-4-generated explanations are relatively 20% less informative than explanations curated by lay humans. this gap is particularly pronounced for users with graduate-level and highschool backgrounds. overall, our results highlight the limitations of current language model-driven pedagogy and suggest that explicitly prompting for audience adaptation alone might be insufficient. we believe that in addition to eli-why being a valuable resource to evaluate language models' pedagogical utility, our human-centered evaluation framework can help evaluate personalized agents catered to the informational needs of individual users. ## the eli-why benchmark existing work in pedagogical evaluation of language models has either focused on objective benchmark-driven question-answering tasks (e.g. multiple-choice science-based question answering) @xcite @xcite or subjective use-case driven tasks (e.g. evaluating academic achievements induced by language model assistants) @xcite . combining these two, we focus on the task of answering "why" questions; they ensure a good balance between having a knowledge-seeking setting and having room for subjectivity in the manner in which knowledge is presented @xcite . to this end, we introduce eli-why, which consists of 13,392 "why" questions curated across stem and non-stem domains. there are 6,217 stem questions (across disciplines like physics, chemistry, computer science, material engineering etc.), and 7,175 non-stem questions (across disciplines like sociology, law, culture, history, public relations, etc.). our dataset is created by (1) over-generating "why" questions from gpt-4 via few-shot prompting followed by (2) extensive filtering by checking validity of the generated questions. we expand upon these steps below, and provide full details, including prompts, model settings and filtering process about eli-why curation in appendix b. overgenerating "why" questions from gpt-4. we use a set of 50 seed "why" questions from @xcite ( ## generating explanations for different educational backgrounds users with varying educational or conceptual backgrounds differ in expectations of answers to their questions @xcite . tailoring responses to users with different educational backgrounds is important to improve language models' use in pedagogy @xcite . in this section, we describe the different educational levels we used for evaluating language model explanations and our methodology for generating grade-tailored explanations. educational backgrounds. we choose three educational levels with different informational needs foot_2 for our users foot_3 : elementary school , high school and graduate school , in the context of education in the united states foot_4 . elementary school group typically covers content up to u.s. grade 4, and adults with this education level may have limited theoretical knowledge of individual disciplines. the high school group extends through u.s. grade 12 to approximately the sophomore year of undergraduate studies, and adults at this level have a foundational grasp of academic subjects but may still struggle with discipline-specific terminology. graduate school group typically follow a bachelor's degree, offering advanced, specialized education and adults with this education have few knowledge gaps and possess expertise in specific areas without needing foundational instruction foot_5 . ## do generated explanations help provide new information to users? a fundamental notion of utility for language models in pedagogical cases is how much they assist users in learning new information @xcite @xcite . in this section, we discuss the utility of explanations in delivering new information to a learner, that aligns with the learner's informational needs. understanding this is crucial in determining whether language models like gpt-4 tailor explanations for different educational backgrounds merely stylistically or if they provide new and relevant information that contributes to learning and comprehension. evaluating informativeness w.r.t user informational needs. consider a user with a high schoollevel background in physics, familiar with basic concepts about light such as such as scattering, wave-particle duality, and light interactions. given a question, "why is the sky blue?", the user receives the following explanation: "because of a @xmath1., high-school, grad.} question does the explanation introduce a new concept, previously unknown to you? elem. grad. high school yes no does the explanation connect the introduced concept(s) to something you may already know? need complex explanation informative, end initial expl. randomly picked no yes need simpler explanation exists doesn't exist unavailable, end figure 5 : user evaluation of explanation informativeness: participants are provided with a randomly selected explanation (from one of the educational backgrounds) for a given question. they then assume the role of a learner and determine if an explanation provides new information that connects with their information needs. human study and metrics. we recruited adult participants with the following highest education levels: elementary school, high school, and graduate degrees in two distinct disciplines-physics for stem and psychology for non-stem. we select 40 questions from eli-why, and derived from section 4.1, we use gpt-4-generated explanations that were perceived to match their intended educational backgrounds. for each educational background and question, participants assume the role of a learner and determine if gpt-4 generates explanations that are informative for a question. each question is answered by five participants, leading to 200 responses for each educational background. we compute two metrics: % informative explanations which is the % of questions where any one of the three gpt-4 gradetailored explanations were found informative, and % matched informative explanations which is the % of questions where explanations were informative and matched the participant's educational background. given that we aim to capture how useful grade-tailored explanations are for an individual and that every individual may have different prior knowledge even within the same educational background, we do not do any majority voting while aggregating the above metrics for a question across participants. lastly, we also replicate the same user study with the 40 eli-why questions with manually web-retrieved explanations. results. figure 6 shows the % informative explanations and % matched informative explanations results for participants with different educational background. we observe that participants with higher education backgrounds have lower % informative explanations and % matched informative explanations. it is particularly stark for participants with a graduate school -physics background, where only 19% of questions have informative explanations that match the participant's background. we find that on an aggregate basis, manually web-retrieved explanations consistently outperform gpt-4 on both metrics across all educational backgrounds. while gpt-4 provides new information at a comparable rate for elementary school and high school participants, its effectiveness declines significantly for graduate school -background participants. on average, for all three educational backgrounds, manually web-retrieved explanations are relatively 20% more informative than gpt-4 explanations. it is important to note that manually web-retrieved explanations are curated by lay experts, not domain experts. these individuals rely on general knowledge, metadata about online resources to craft responses, yet they still provide more informative and better-aligned explanations than gpt-4. this suggests that gpt-4 struggles not just with domain expertise, but also with the broader research and adaptation strategies that even nonexperts employ when tailoring explanations. recruiting actual subject matter experts could further widen this gap, highlighting limitations in delivering truly audience-appropriate information. ## conclusion our study introduces eli-why, a benchmark for evaluating the pedagogical utility of language model explanations tailored to users belonging to different educational backgrounds. through user studies, we find that language models like gpt-4 struggle to align explanations with intended ed-ucational levels and fail to be informative, especially for advanced learners. automated evaluations across multiple model families confirm that grade-tailored explanations often collapse into a similar and narrow complexity range, hinting at their limited effectiveness. future work can explore methods for integrating measures of pedagogical utility for both educators and learners, as a signal to improve language models for users and cater to more personal learning goals, not bounded by educational backgrounds. as anonymous reviewers and meta reviewers for their comments and feedback on the draft. this research is supported in part by the national science foundation (nsf) under grant iis2403437, the simons foundation, and the allen institute for ai, the office of the director of national intelligence (odni), intelligence advanced research projects activity (iarpa), via the hiatus program contract #2022-22072200006. the views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of nsf, odni, iarpa, or the u.s. government. the u.s. government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. b. joshi was supported by the apple scholars in ai/ml phd fellowship. this work was partially done when s. swayamdipta was a visitor at the simons institute for the theory of computing.