@inproceedings{maksimchuk-etal-2025-generative,
title = "Generative {AI} in the K{--}12 Formative Assessment Process: Enhancing Feedback in the Classroom",
author = "Maksimchuk, Mike Thomas and
Roeber, Edward and
Store, Davie",
editor = "Wilson, Joshua and
Ormerod, Christopher and
Beiting Parrish, Magdalen",
booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers",
month = oct,
year = "2025",
address = "Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States",
publisher = "National Council on Measurement in Education (NCME)",
url = "https://aclanthology.org/2025.aimecon-main.12/",
pages = "107--110",
ISBN = "979-8-218-84228-4",
abstract = "This paper explores how generative AI can enhance K{--}12 formative assessment by improving feedback, supporting task design, fostering student metacognition, and building teacher assessment literacy. It addresses challenges of equity, ethics, and implementation, offering practical strategies and case studies to guide responsible AI integration in classroom formative assessment practices."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="maksimchuk-etal-2025-generative">
<titleInfo>
<title>Generative AI in the K–12 Formative Assessment Process: Enhancing Feedback in the Classroom</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mike</namePart>
<namePart type="given">Thomas</namePart>
<namePart type="family">Maksimchuk</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Edward</namePart>
<namePart type="family">Roeber</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Davie</namePart>
<namePart type="family">Store</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers</title>
</titleInfo>
<name type="personal">
<namePart type="given">Joshua</namePart>
<namePart type="family">Wilson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Christopher</namePart>
<namePart type="family">Ormerod</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Magdalen</namePart>
<namePart type="family">Beiting Parrish</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>National Council on Measurement in Education (NCME)</publisher>
<place>
<placeTerm type="text">Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-218-84228-4</identifier>
</relatedItem>
<abstract>This paper explores how generative AI can enhance K–12 formative assessment by improving feedback, supporting task design, fostering student metacognition, and building teacher assessment literacy. It addresses challenges of equity, ethics, and implementation, offering practical strategies and case studies to guide responsible AI integration in classroom formative assessment practices.</abstract>
<identifier type="citekey">maksimchuk-etal-2025-generative</identifier>
<location>
<url>https://aclanthology.org/2025.aimecon-main.12/</url>
</location>
<part>
<date>2025-10</date>
<extent unit="page">
<start>107</start>
<end>110</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Generative AI in the K–12 Formative Assessment Process: Enhancing Feedback in the Classroom
%A Maksimchuk, Mike Thomas
%A Roeber, Edward
%A Store, Davie
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
%D 2025
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-218-84228-4
%F maksimchuk-etal-2025-generative
%X This paper explores how generative AI can enhance K–12 formative assessment by improving feedback, supporting task design, fostering student metacognition, and building teacher assessment literacy. It addresses challenges of equity, ethics, and implementation, offering practical strategies and case studies to guide responsible AI integration in classroom formative assessment practices.
%U https://aclanthology.org/2025.aimecon-main.12/
%P 107-110
Markdown (Informal)
[Generative AI in the K–12 Formative Assessment Process: Enhancing Feedback in the Classroom](https://aclanthology.org/2025.aimecon-main.12/) (Maksimchuk et al., AIME-Con 2025)
ACL