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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. 

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. 

Factors influencing students' perceptions of automated feedback and their impact on revision. 

Automated feedback can provide students with timely information about their writing, but students' willingness to engage meaningfully with the feedback to revise their writing may be influenced by their perceptions of its usefulness. We explored the factors that may have influenced 339, 8th-grade students’ perceptions of receiving automated feedback on their writing and whether their perceptions impacted their revisions and writing improvement. Using HLM and logistic regression analyses, we found that: 1) students with more positive perceptions of the automated feedback made revisions that resulted in significant improvements in their writing, and 2) students who received feedback indicating they included more important ideas in their essays had significantly higher perceptions of the usefulness of the feedback, but were significantly less likely to engage in substantive revisions. Implications and the importance of helping students evaluate and reflect on the feedback to make substantive revisions, no matter their initial feedback, are discussed 

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. 

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. 

Abstract Molecular profiles of mesenchymal stem cells (MSCs) are needed to standardize the composition and effectiveness of MSC therapeutics. This study employs RNA sequencing to identify genes to be used in concert with CD264 as a molecular profile of aging MSCs at a clinically relevant culture passage. CD264⁻and CD264⁺populations were isolated by fluorescence-activated cell sorting from passage 4 MSC cultures. CD264⁺MSCs exhibited an aging phenotype relative to their CD264⁻counterpart. Donor-matched CD264^−/+mRNA samples from 5 donors were subjected to pair-ended, next-generation sequencing. An independent set of 5 donor MSCs was used to validate differential expression of select genes with quantitative reverse transcription PCR. Pairwise differential expression analysis identified 2,322 downregulated genes and 2,695 upregulated genes in CD264⁺MSCs relative to donor-matched CD264⁻MSCs with a Benjamini–Hochberg adjustedp-value (BHp_adj) < 0.1. Nearly 25% of these genes were unique to CD264^−/+MSCs and not differentially expressed at a significance level of BHp_adj < 0.1 in previous RNA sequencing studies of early- vs. late-passage MSCs. Least Absolute Shrinkage and Selection Operator regression identified microtubule-associated protein 1A (MAP1A) and pituitary tumor-transforming gene 1 (PTTG1) as predictive genes of CD264⁺MSCs. CombinedMAP1AandPTTG1expression correctly classified CD264 status of MSC samples with an accuracy of 100%. Differential expression and predictive ability ofMAP1AandPTTG1compared favorably with that of existing senescence markers expressed in early passage CD264^−/+MSCs. This study provides the first linkage ofMAP1Ato CD264, aging and senescence. Our findings have application as quality metrics to standardize the composition of MSC therapies and as molecular targets to slow/reverse cellular aging. 

The present study examined teachers’ conceptualization of the role of AI in addressing inequity. Grounded in speculative design and education, we examined eight secondary public teachers’ thinking about AI in teaching and learning that may go beyond present horizons. Data were collected from individual interviews. Findings suggest that not only equity consciousness but also present engagement in a context of inequities were crucial to future dreaming of AI that does not harm but improve equity. 

In this paper, we present a case study of designing AI-human partnerships in a realworld context of science classrooms. We designed a classroom environment where AI technologies, teachers and peers worked synergistically to support students’ writing in science. In addition to an NLP algorithm to automatically assess students’ essays, we also designed (i) feedback that was easier for students to understand; (ii) participatory structures in the classroom focusing on reflection, peer review and discussion, and (iii) scaffolding by teachers to help students understand the feedback. Our results showed that students improved their written explanations, after receiving feedback and engaging in reflection activities. Our case study illustrates that Augmented Intelligence (USDoE, 2023), in which the strengths of AI complement the strengths of teachers and peers, while also overcoming the limitations of each, can provide multiple forms of support to foster learning and teaching.