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   
                    
                            
                            Examining the effect of automated assessments and feedback on students’ written science explanations
                        
                    
    
            Writing scientific explanations is a core practice in science. However, students find it difficult to write coherent scientific explanations. Additionally, teachers find it challenging to provide real-time feedback on students’ essays. In this study, we discuss how PyrEval, an NLP technology, was used to automatically assess students’ essays and provide feedback. We found that students explained more key ideas in their essays after the automated assessment and feedback. However, there were issues with the automated assessments as well as students’ understanding of the feedback and revising their essays. 
        more » 
        « less   
        
    
                            - Award ID(s):
- 2010483
- PAR ID:
- 10418173
- Publisher / Repository:
- International Society for the Learning Sciences
- Date Published:
- Journal Name:
- Computer Supported Collaborative Learning Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
- 
            
- 
            Automated methods are becoming increasingly used to support formative feedback on students’ science explanation writing. Most of this work addresses students’ responses to short answer questions. We investigate automated feedback on students’ science explanation essays, which discuss multiple ideas. Feedback is based on a rubric that identifies the main ideas students are prompted to include in explanatory essays about the physics of energy and mass. We have found that students revisions generally improve their essays. Here, we focus on two factors that affect the accuracy of the automated feedback. First, learned representations of the six main ideas in the rubric differ with respect to their distinctiveness from each other, and therefore the ability of automated methods to identify them in student essays. Second, sometimes a student’s statement lacks sufficient clarity for the automated tool to associate it more strongly with one of the main ideas above all others.more » « less
- 
            Hoadley, C; Wang, XC (Ed.)Helping students learn how to write is essential. However, students have few opportunities to develop this skill, since giving timely feedback is difficult for teachers. AI applications can provide quick feedback on students’ writing. But, ensuring accurate assessment can be challenging, since students’ writing quality can vary. We examined the impact of students’ writing quality on the error rate of our natural language processing (NLP) system when assessing scientific content in initial and revised design essays. We also explored whether aspects of writing quality were linked to the number of NLP errors. Despite finding that students’ revised essays were significantly different from their initial essays in a few ways, our NLP systems’ accuracy was similar. Further, our multiple regression analyses showed, overall, that students’ writing quality did not impact our NLP systems’ accuracy. This is promising in terms of ensuring students with different writing skills get similarly accurate feedback.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
 An official website of the United States government
An official website of the United States government 
				
			 
					 
					
 
                                    