An official website of the United States government
This study is part of a larger research project aimed at developing and implementing an NLP-enabled AI feedback tool called PyrEval to support middle school students’ science explanation writing. We explored how human-AI integrated classrooms can invite students to harness AI tools while still being agentic learners. Building on theory of new materialism with posthumanist perspectives, we examined teacher framing to see how the nature of PyrEval was communicated, thereby orienting students to partner with or rely on PyrEval. We analyzed one teacher’s talk in multiple classrooms as well as that of students in small groups. We found student agency was fostered through teacher framing of (a) PyrEval as a non-neutral actor and a co-investigator and (b) students’ participation as an author and their understanding of the nature of PyrEval as core task and purpose. Findings and implications are discussed.
more »
« less
This content will become publicly available on July 9, 2026
The role of teacher framing in shaping student agency in human-AI partnered science classrooms
This study is part of a larger research project aimed at developing and implementing an NLP-enabled AI feedback tool called PyrEval to support middle school students’ science explanation writing. We explored how human-AI integrated classrooms can invite students to harness AI tools while still being agentic learners. Building on theory of new materialism with posthumanist perspectives, we examined teacher framing to see how the nature of PyrEval was communicated, thereby orienting students to partner with or rely on PyrEval. We analyzed one teacher’s talk in multiple classrooms as well as that of students in small groups. We found student agency was fostered through teacher framing of (a) PyrEval as a non-neutral actor and a co-investigator and (b) students’ participation as an author and their understanding of the nature of PyrEval as core task and purpose. Findings and implications are discussed.
more »
« less
- Award ID(s):
- 2010483
- PAR ID:
- 10615063
- Publisher / Repository:
- Proceedings of the International Society of the Learning Sciences
- Date Published:
- Journal Name:
- Proceedings
- ISSN:
- 1814-9316
- ISBN:
- 979-8-9906980-3-1
- Format(s):
- Medium: X
- Location:
- Helsinki, Finland
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract As use of artificial intelligence (AI) has increased, concerns about AI bias and discrimination have been growing. This paper discusses an application called PyrEval in which natural language processing (NLP) was used to automate assessment and provide feedback on middle school science writing without linguistic discrimination. Linguistic discrimination in this study was operationalized as unfair assessment of scientific essays based on writing features that are not considered normative such as subject‐verb disagreement. Such unfair assessment is especially problematic when the purpose of assessment is not assessing English writing but rather assessing the content of scientific explanations. PyrEval was implemented in middle school science classrooms. Students explained their roller coaster design by stating relationships among such science concepts as potential energy, kinetic energy and law of conservation of energy. Initial and revised versions of scientific essays written by 307 eighth‐grade students were analyzed. Our manual and NLP assessment comparison analysis showed that PyrEval did not penalize student essays that contained non‐normative writing features. Repeated measures ANOVAs and GLMM analysis results revealed that essay quality significantly improved from initial to revised essays after receiving the NLP feedback, regardless of non‐normative writing features. Findings and implications are discussed. Practitioner notesWhat is already known about this topicAdvancement in AI has created a variety of opportunities in education, including automated assessment, but AI is not bias‐free.Automated writing assessment designed to improve students' scientific explanations has been studied.While limited, some studies reported biased performance of automated writing assessment tools, but without looking into actual linguistic features about which the tools may have discriminated.What this paper addsThis study conducted an actual examination of non‐normative linguistic features in essays written by middle school students to uncover how our NLP tool called PyrEval worked to assess them.PyrEval did not penalize essays containing non‐normative linguistic features.Regardless of non‐normative linguistic features, students' essay quality scores significantly improved from initial to revised essays after receiving feedback from PyrEval. Essay quality improvement was observed regardless of students' prior knowledge, school district and teacher variables.Implications for practice and/or policyThis paper inspires practitioners to attend to linguistic discrimination (re)produced by AI.This paper offers possibilities of using PyrEval as a reflection tool, to which human assessors compare their assessment and discover implicit bias against non‐normative linguistic features.PyrEval is available for use ongithub.com/psunlpgroup/PyrEvalv2.more » « less
-
As use of artificial intelligence (AI) has increased, concerns about AI bias and discrimination have been growing. This paper discusses an application called PyrEval in which natural language processing (NLP) was used to automate assessment and pro- vide feedback on middle school science writing with- out linguistic discrimination. Linguistic discrimination in this study was operationalized as unfair assess- ment of scientific essays based on writing features that are not considered normative such as subject- verb disagreement. Such unfair assessment is espe- cially problematic when the purpose of assessment is not assessing English writing but rather assessing the content of scientific explanations. PyrEval was implemented in middle school science classrooms. Students explained their roller coaster design by stat- ing relationships among such science concepts as potential energy, kinetic energy and law of conser- vation of energy. Initial and revised versions of sci- entific essays written by 307 eighth- grade students were analyzed. Our manual and NLP assessment comparison analysis showed that PyrEval did not pe- nalize student essays that contained non-normative writing features. Repeated measures ANOVAs and GLMM analysis results revealed that essay quality significantly improved from initial to revised essays after receiving the NLP feedback, regardless of non- normative writing features. Findings and implications are discussed.more » « less
-
As use of artificial intelligence (AI) has increased, concerns about AI bias and discrimination have been growing. This paper discusses an application called PyrEval in which natural language processing (NLP) was used to automate assessment and pro- vide feedback on middle school science writing with- out linguistic discrimination. Linguistic discrimination in this study was operationalized as unfair assess- ment of scientific essays based on writing features that are not considered normative such as subject- verb disagreement. Such unfair assessment is espe- cially problematic when the purpose of assessment is not assessing English writing but rather assessing the content of scientific explanations. PyrEval was implemented in middle school science classrooms. Students explained their roller coaster design by stat- ing relationships among such science concepts as potential energy, kinetic energy and law of conser- vation of energy. Initial and revised versions of sci- entific essays written by 307 eighth- grade students were analyzed. Our manual and NLP assessment comparison analysis showed that PyrEval did not pe- nalize student essays that contained non-normative writing features. Repeated measures ANOVAs and GLMM analysis results revealed that essay quality significantly improved from initial to revised essays after receiving the NLP feedback, regardless of non- normative writing features. Findings and implications are discussed.more » « less
-
This research employs the lenses of epistemological resources and framing to examine the complexities of one teacher’s efforts to position his middle-school biology students as sensemakers. Through interviews, classroom observations, and document analysis, we trace the teacher’s activation of varied epistemological resources and how such resources positioned students’ efforts throughout the lesson. While the launch of tasks was framed as an opportunity for “doing science,” this framing became less stable when the teacher engaged with students in small group work and during the wrap up that were focused on the “right answer.” Specific phases of the lesson served as a context that influenced the epistemological resources activated, helping us understand the varied, dynamic, and sometimes contradictory nature of the teacher’s moves and their consequences on students’ framing of their efforts.more » « less
