Challenges and opportunities for classroom-based formative assessment and AI: a perspective article

Abstract

The integration of artificial intelligence (AI) into educational contexts may give rise to both positive and negative ramifications for teachers’ uses of formative assessment within their classrooms. Drawing on our diverse experiences as academics, researchers, psychometricians, teachers, and teacher educators specializing in formative assessment, we examine the pedagogical practices in which teachers provide feedback, facilitate peer- and self-assessments, and support students’ learning, and discuss how existing challenges to each of these may be affected by applications of AI. Firstly, we overview the challenges in the practice of formative assessment independently of the influence of AI. Moreover, based on the authors’ varied experience in formative assessment, we discuss the opportunities that AI brings to address the challenges in formative assessment as well as the new challenges introduced by the application of AI in formative assessment. Finally, we argue for the ongoing importance of self-regulated learning and a renewed emphasis on critical thinking for more effective implementation of formative assessment in this new AI-driven digital age.

Full text

Frontiers in Education 01 frontiersin.org
Challenges and opportunities for
classroom-based formative
assessment and AI: a perspective
article
ThereseN.Hopfenbeck
1,2, 3*, ZhonghuaZhang
1, Sundance
ZhihongSun
1, PamRobertson
1 and JoshuaA.McGrane
1,2
1 Assessment and Evaluation Research Centre, Graduate School of Education, The University of
Melbourne, Parkville, VIC, Australia, 2 Kellogg College, University of Oxford, Oxford, England, 3 The
University of Science and Technology, Trondheim, Norway
The integration of artificial intelligence (AI) into educational contexts may give rise to
both positive and negative ramifications for teachers’ uses of formative assessment
within their classrooms. Drawing on our diverse experiences as academics, researchers,
psychometricians, teachers, and teacher educators specializing in formative
assessment, weexamine the pedagogical practices in which teachers provide feedback,
facilitate peer- and self-assessments, and support students’ learning, and discuss how
existing challenges to each of these may be aected by applications of AI. Firstly,
we overview the challenges in the practice of formative assessment independently
of the influence of AI. Moreover, based on the authors’ varied experience in formative
assessment, wediscuss the opportunities that AI brings to address the challenges in
formative assessment as well as the new challenges introduced by the application
of AI in formative assessment. Finally, weargue for the ongoing importance of self-
regulated learning and a renewed emphasis on critical thinking for more eective
implementation of formative assessment in this new AI-driven digital age.
KEYWORDS
artificial intelligence, formative assessment, self-regulation, critical thinking, classroom
based assessment
Introduction
In an era marked by rapid technological advancements, articial intelligence (AI) is now
increasingly used in diverse sectors of our society, fundamentally transforming the way welive,
work, and learn. Within the eld of educational assessment, the introduction of AI has raised
both concerns and optimisms, particularly with respect to the dynamics around AI and
formative assessment classroom practices. In the current paper, weexplore the opportunities
and challenges AI oers and underscore the continued signicance of self-regulated learning
and critical thinking as essential skills in this AI -driven digital age.
A brief background of classroom-based formative
assessment
Classroom-based assessment has been internationally researched for decades, both
with respect to summative assessments that typically occur at the end of a learning
process (e.g., McMillan, 2013; Brookhart, 2016), as well as formative assessments that
OPEN ACCESS
EDITED BY
Gavin T. L. Brown,
The University of Auckland, NewZealand
REVIEWED BY
Syamsul Nor Azlan Mohamad,
MARA University of Technology, Malaysia
Jason M. Lodge,
The University of Queensland, Australia
Kim Schildkamp,
University of Twente, Netherlands
*CORRESPONDENCE
Therese N. Hopfenbeck
Therese.hopfenbeck@unimelb.edu.au
RECEIVED 01 August 2023
ACCEPTED 02 November 2023
PUBLISHED 23 November 2023
CITATION
Hopfenbeck TN, Zhang Z, Sun SZ,
Robertson P and McGrane JA (2023)
Challenges and opportunities for classroom-
based formative assessment and AI: a
perspective article.
Front. Educ. 8:1270700.
doi: 10.3389/feduc.2023.1270700
COPYRIGHT
© 2023 Hopfenbeck, Zhang, Sun, Robertson
and McGrane. This is an open-access article
distributed under the terms of the Creative
Commons Attribution License (CC BY). The
use, distribution or reproduction in other
forums is permitted, provided the original
author(s) and the copyright owner(s) are
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TYPE Perspective
PUBLISHED 23 November 2023
DOI 10.3389/feduc.2023.1270700

Hopfenbeck et al. 10.3389/feduc.2023.1270700
Frontiers in Education 02 frontiersin.org
involve feedback processes promoting students’ learning as it
happens (e.g., Brown, 2018; Lipnevich and Smith, 2018). Black
and Wiliam (1998) emphasized the pivotal role of formative
assessment in providing valuable information not only to
teachers but also to students, guiding improvements in teaching
and learning to optimize student outcomes. Since the publication
of their classic work, they have continued to refine their model
through subsequent theoretical papers (e.g., Black and Wiliam,
1998). Additionally, they have supported their theoretical
insights with empirical studies, documenting the tangible impact
of formative assessment practices on students’ learning within
classroom settings (e.g., Wiliam etal., 2004).
While there is a consensus among researchers regarding the
positive effects of formative assessment on students’ learning
(Hattie 2009; Lipnevich and Smith, 2018), the term “formative
assessment” itself has faced critique for lacking a cohesive
definition. Instead, it has been argued to bea collection of
varied definitions and practices, making it challenging to
conduct rigorous evaluations of its effects (Bennett 2011;
Stobart and Hopfenbeck, 2014). We aim to navigate these
complexities by adopting Black and Wiliam’s (2009) definition
of formative assessment: “Practice in a classroom is formative
to the extent that the evidence about student achievement is
elicited, interpreted, and used by teachers, learners, and their
peers to make decisions about their next steps in instruction
that are likely to bebetter, or better founded than the decisions
they would have taken in the absence of the elicited
evidence” (p.9).
In other words, for a teacher to conduct formative assessment,
they will need to know each student, their learning progress,
and how to support them to achieve their learning goals. In
traditional classrooms, formative assessment challenges teachers,
as it requires them to find ways of following up a whole class or
classes of students and provide individualized feedback to
everyone, either through teacher-assessment, peer assessment,
self-assessment, group-assessment, or by other means (Double
etal., 2020). As wewill discuss in the next section, research has
shown that these practices are difficult to implement at scale and
in ways that are sustainable over time (Hopfenbeck and
Stobart, 2015).
Challenges to implementing formative
assessment
Several challenges to the implementation of formative assessment
have been documented by researchers as presented in the January
2015 Special Issue of the Assessment in Education journal. Wylie and
Lyon (2015) found substantial variation in the quality of implementing
formative assessments among 202 Mathematics and Science teachers
in the US context. ey suggest that more targeted professional
development is needed to secure high-quality implementations of
formative assessment practices. Further, since formative assessment
requires teachers to have high competency across a range of knowledge
and skills (e.g., domain content knowledge, pedagogical content
knowledge, assessment and data literacy, and knowledge of
measurement fundamentals), such professional development needs to
bewide in scope.
Challenges have also been found when stakeholders involved
in the assessment do not share a mutual understanding of its
purpose(s). For example, in the same Special Issue, Hopfenbeck
et al. (2015) conducted an evaluation of a large-scale
implementation of an assessment for learning program in Norway
and found that implementation was weaker in schools where the
assessment was perceived as part of an accountability system, while
in schools with a high degree of trust between teachers, head
teachers and the school owners at the municipality level, the
quality of the implementation was better. Similarly, a study of
school-based assessment in Singapore found that their high-stakes
examination-focused education system created tensions when
trying to implement formative assessment processes, thus
demonstrating how context matters in terms of the challenges that
arise between dierent stakeholders in the interaction between
formative assessment and accountability systems (Ratnam-Lim
and Tan, 2015). ese ndings indicate how teachers’ formative
assessment implementations are inuenced by accountability
structures, educational leadership, resources, workload and social
pressures within their context.
irdly, formative assessment practices have primarily been
researched and developed in contexts where students and teachers
have access to a wealth of resources, and, thus, do not necessarily
generalize to more challenging contexts. Halai etal. (2022) evaluated
an implementation of assessment for learning practices in six schools
in Dar es Salaam, Tanzania, and documented the challenges created
by very large class sizes, with one teacher responsible for up to 180
students at a time, as well as under-resourced classrooms.
Furthermore, it was found that the cultural assumptions of the
student role assumed in most of the Western, English-speaking
literature did not t what is seen as a good student in Tanzania.
Formative assessment practices expect students to beself-regulated
and proactive so they can participate in peer-discussions and
assessment, and as part of this, they are supposed to engage in dialog
in groups and with the teacher and beable to ask critical questions.
In contrast, a good student in Tanzania is expected to listen to the
teacher, not ask questions or be too critical, and overall follow
instructions and do what the teacher tells them to do. is is enforced
by the parents’ expectations of how schools and teachers need to help
raise the child. us, the interactive dialog between teachers, students
and peers that are at the heart of formative assessment can
beculturally and contextually sensitive, which poses challenges for
implementing a ‘one size ts all’ formative assessment practice across
dierent contexts.
Finally, studies have reported that teachers nd it challenging to
provide enough feedback to students, particularly at crucial times in
the learning process, as well as with the quality of feedback required
to further each student’s learning, due to time and other resource
constraints (Brooks etal., 2019; Gamlem and Vattøy, 2023). As a
result, formative assessment theory suggests that teachers need to
design classrooms where students can provide feedback to each other
to help reduce this workload (Wiliam, 2011). However, teachers still
report that they struggle to manage classrooms where these peer
assessment practices are established (Dignath etal., 2008; Halai etal.,
2022). So, even in well-resourced contexts where teachers endeavor to
engage best-practices in the implementation of formative assessment
in their classrooms, the high workload such practices engender
continues to bea barrier.

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Evolving and revolutionizing formative
assessment: what can AI bring?
Given the challenges to implementing formative assessment in the
classroom that are outlined in the previous section, the advancement
of AI and relevant products (e.g., ChatGPT) may provide opportunities
to overcome some of these challenges, such as high number of
students, and only one teacher to provide feedback. Indeed,
researchers have identied assessment as one of the most signicant
areas of opportunity that AI and related technologies oer in
education (Cope etal., 2021; Swiecki etal., 2022; Zhai and Nehm,
2023). However, new challenges may also beintroduced through the
widespread use of AI, including practical and ethical challenges
(Milano etal., 2023). is section focuses on the changing landscape
of the practice of formative assessment, especially under the inuence
of AI, and discusses how AI can help support teachers to provide
formative assessment for students on a large and sustainable scale.
What do wemean by AI?
In our discussion, AI refers to the application of sophisticated
algorithms that allow computers and machines to simulate human
intelligence for successfully completing tasks (Murphy, 2019).
Although dierent technical approaches and methods, e.g., supervised
learning versus deep learning, have been used to develop AI systems,
the essence of AI is to use data to teach machines to make
classications, diagnoses, decisions, predictions, and/or
recommendations (Gardner et al., 2021). More specically, the
application of AI typically involves collecting large multivariate data
relevant to the task of interest, applying statistical methods and
sophisticated algorithms to process sets of input data to build a
model(s) that identies and weights features and/or patterns of the
input variables relevant to the task, then using a dierent pre-collected
dataset to validate the model(s) for the task where the correct output
is known in advance, and then applying the model(s) to generate a
task output(s) (e.g., classication, prediction, decision, or
recommendation) in a context where the correct output is unknown
(Murphy, 2019; Gardner etal., 2021).
Opportunities for implementing formative
assessment with AI
As mentioned above, one of the challenges in formative
assessment is to provide individualized, high-quality feedback to
students. It is highly resource intensive for teachers to personally give
or nd other ways (e.g., peer assessment, self-assessment, group
assessment) to provide individualized feedback to each student on a
large scale. However, AI can make some of the assessment procedures
fully or partly automated, making the assessment practices more
feasible to maintain, which can then reduce the time burden on
teachers (Swiecki etal., 2022). A typical example that has been widely
discussed is automated essay scoring systems (Ke and Ng, 2019;
Gardner etal., 2021). e application of AI in automated essay scoring
frees teachers from the labor-intensive grading process and allows
them to assign more extended writing tasks to students, automate the
grading process, and, more importantly, with the integration of
natural language processing-based AI, provide timely formative
feedback to help students revise and improve their writing
(Murphy, 2019).
Liu et al. (2016) showed that a machine learning enabled
automated scoring tool, c-rater-ML, could produce scores that were
comparable to human raters in scoring students’ responses to
constructed response questions about science inquiry, oering a
promising solution to improving the eciency of not only obtaining
the summative scores but also generating instant formative feedback
(Linn etal., 2014). Another example of how AI can help is by using
computers to support the management and delivery of formative
assessments (e.g., Webb et al., 2013; Tomasik etal., 2018). ese
systems have the capacity to discern distinct learning pathways in
students’ progress, enabling the identication of the most suitable
tasks or questions for each student at dierent points in time. In
addition, computerized formative assessment systems can optimize
the administration of formative assessments by determining their
frequencies and schedules customized for every individual student
(Shin etal., 2022). ese ndings demonstrate how AI can improve
the eciency and exibility of formative assessment practices at the
individual student level.
Another signicant opportunity that AI oers for formative
assessment is the improvement of feedback both in quantity and
quality (Gardner etal., 2021). e main goals of formative assessment
are to provide constructive feedback based on students’ responses and
to help teachers design dierentiated instructional strategies and
sustain students in self-regulating their learning. AI can delve into the
data to identify the patterns on which dynamic, customized,
individualized, and visualized feedback can beautomatically generated
(Verma, 2018; Tashu and Horvath, 2019; Lee, 2021, 2023). For
example, the adaptive nature of some computerized formative
assessment systems and intelligent tutoring systems enables every
student’s attainment to beindividually and more precisely assessed,
which facilitates more appropriate and targeted feedback based on
their individual learning stage and trajectory (Ma etal., 2014; Tomasik
et al., 2018; Mousavinasab et al., 2021). Adaptive multi-strategy
feedback models, based on AI methods, have been applied in the
context of such systems to automatically adapt the feedback generating
strategy to individual students, which have, in turn, been found to
generate more eective feedback than the traditional feedback
generation methods (Gutierrez and Atkinson, 2011).
In addition, AI can improve the quality and eectiveness of peer
assessment in classrooms with large class sizes. Peer assessment can
besupported with prompts from language models (Er etal., 2021).
is approach to peer assessment supports students in not only
providing feedback to peers but also reecting on and justifying their
judgments, providing further opportunities for them to develop their
self-regulated learning skills (Liu and Carless, 2006), and has been
found to provide useful peer feedback to students (Luaces etal., 2018).
In addition, the peer assessment reviews can both help the teachers to
better understand the performance of the students in their classroom
and also provide additional data (e.g., the review text) that can
beanalyzed using AI-based techniques (e.g., semantic, lexical, and
psycho-linguistic analyses; Vincent-Lamarre and Larivière, 2021) to
further enhance teachers’ understanding of the performance of
their students.
AI can also aid teachers in collecting and analysing longitudinal
formative assessment data, and in generating learner proles to trace

Hopfenbeck et al. 10.3389/feduc.2023.1270700
Frontiers in Education 04 frontiersin.org
students’ learning progression over time (Swiecki etal., 2022). is
application of AI enables the scalable implementation of formative
assessment in both cross-sectional and longitudinal contexts, which
makes it more sustainable, allowing teachers to eciently monitor the
growth of student learning and identify the knowledge and skill gaps
in their learning over time (Barthakur et al., 2023). Another
contribution of AI to facilitating longitudinal formative assessment
lies in its ability to analyse the large-scale longitudinal formative
assessment data to trace the learning trajectories of the students and
predict their future learning states. For example, some of the widely
applied statistical methods in AI (e.g., hidden-Markov models,
articial neural networks) have been combined with traditional
cognitive diagnostic models (CDM) to analyse longitudinal formative
assessment data to track the changes of students’ learning over time
(e.g., Chen etal., 2018; Wen etal., 2020).
e application of AI also allows teachers to get an in-depth
understanding of students’ learning processes based on the analysis of
large volumes of ‘process data’ rather than just the assessment artefacts
(e.g., responses to questions, items or tasks) produced by the students.
With modern technologies, the processes leading to assessment
responses can becaptured in time-stamped log stream data (Cope
etal., 2021). For example, students’ actions (e.g., keystrokes, editing,
chat history, video watching) during an assessment can becaptured.
Which potentially contain additional information for understanding
how students produce their responses. With the support of AI, the
process data can be analyzed to investigate their strategies (e.g.,
identifying misconceptions), which can then provide invaluable
information for individualized feedback. In addition, taking advantage
of AI’s increasing capacity to deal with complex, multi-media data,
more authentic assessment tasks (e.g., multimedia, game-based
problem solving, essay writing, performance-based tasks) can
beeectively used in undertaking formative assessments (Swiecki
etal., 2022).
AI also oers opportunities for assessing some hard-to-measure
constructs. Students’ non-scholastic attributes, including social–
emotional traits (e.g., classroom engagement, self-ecacy, motivation,
resilience) and social-cognitive skills (e.g., metacognition,
collaborative problem-solving, critical thinking, digital literacy, self-
regulated learning), have been attracting more attention and are
increasingly recognized as equally important as their scholastic
achievement (Durlak and Weissberg, 2011). Advances in AI and
related technologies allow for these constructs to be more validly
assessed, instead of purely relying on students’ self-reported beliefs
and behavior through questionnaires. Now, the data collected through
dierent channels (e.g., time-stamped process data, eye contact,
feedback, facial expression, eye movements, body posture and gesture)
can bemined to develop indicators for assessing these dierent aspects
of student learning. For example, MOOC data has been used to design
indicators through a thorough analysis of students learning behaviors
in online courses to measure students’ self-regulated learning (e.g.,
Milligan and Grin, 2016) and leadership development in workplace
learning in an online environment (e.g., Barthakur et al., 2022).
Another example is the measurement of collaborative problem-solving
skills through process data that captured the actions and chats of pairs
of team members collaboratively solving tasks (Grin and Care,
2014). AI-based Large Language Models provide further promise for
mining chat history data to support assessing how team members
explore, dene, plan, execute and solve tasks in a collaborative way.
Challenges arising from using AI in
formative assessment
AI introduces not only opportunities but also challenges to
formative assessment practices (Swiecki etal., 2022). A primary
challenge that needs to beaddressed before teachers can apply AI
in their formative assessment practices is their lack of knowledge
and skills relevant to AI techniques as well as their limited access
to big data. us, although AI can potentially ease the workload of
teachers by automating some aspects of formative assessment (e.g.,
automatising scoring and tracing students’ learning progress), it
adds further burden through the need for professional development
in its use (Engeness, 2021). Moreover, despite the promising future
for formative assessment brought by big data, with the possibility
of collecting the process data through students’ learning, a new
challenge arises in identifying which part of the collected data is
most helpful and relevant to improve student learning. In addition,
the unique features of current big data (e.g., time-stamped process
data, sparse data) are signicantly dierent from that of the
traditional assessment data and pose a variety of challenges to the
psychometric methods for analyzing the data. To deal with this,
scholars have been endeavoring to introduce new methods to
integrate data science and machine learning into psychometrics
(e.g., von Davier etal., 2022).
Another challenge arising from using AI in formative assessment
is to tackle relevant issues about investigating the best way to use AI
in formative assessment practices. One of the hotly debated issues is
whether AI will replace teachers. We argue that AI should not
replace but facilitate teachers’ formative assessment practices and
promote the role of formative assessments in supporting instruction
and learning. As stated by Murphy (2019), “the best use of AI in
education is to augment teacher capacity by helping teachers deliver
more eective classroom instruction” (p. 14). Teachers need to
understand the limitations of the AI techniques when they review
the assessment results. For example, automated scoring systems have
long been criticized for their inability to measure higher-order
aspects of writing (e.g., creativity, argumentation, reasoning)
(Gardner et al., 2021). One of the primary aims of formative
assessment is to diagnose gaps in students’ learning based on the
well-established interpretability of the measurement scales. However,
many approaches based on machine learning are designed for
prediction involving complicated models for improving accuracy but
sacricing the ease of interpretation. erefore, any inferences from
the results of formative assessments involving the integration of AI
techniques should only be made aer having examined the
assessments’ validity and interpretability (Bejar etal., 2016; Scalise
etal., 2021). Teachers need to critically review how the assessment
results are reached and identify any sources of bias introduced by the
application of AI techniques in assessment, which in turn adds to
their workloads (Murphy, 2019). Finally, but not at least, the
introduction of AI in the classroom cannot happen without ethical
considerations for the use and application of it. Scholars have
emphasized the importance of having conversations with students
on the productive, ethical and critical relationship around the use of
AI and future technologies (Bearman etal., 2023) and improving
knowledge on data privacy for children (Johnston, 2023). With these
considerations in mind, we will now turn to one example of
formative assessment practices and AI.

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The formative uses of rubrics and the
opportunities and challenges of using AI
e formative use of rubrics, i.e., the scoring guides that are used
to make judgments about the quality of students’ constructed
responses, such as writing, performances or products (Popham, 1997),
has been shown to have a positive inuence on learning. Specically,
Panadero and Jönsson (2013) argued that the use of quality rubrics
plays a key role in enhancing the eectiveness of formative assessment
practices. ere are two main ways that rubrics are thought to improve
formative assessment. ey make assessment expectations explicit,
thereby assisting with understanding and feedback, and they support
self-regulated learning by supporting learners to monitor their own
learning and make decisions about areas for improvement (Jönsson
and Panadero, 2016). While the use of rubrics for formative assessment
purposes has a positive eect on learning, this eect is amplied when
it is accompanied by teacher-given rubric feedback that addresses the
three feedback questions (Wollenschläger etal., 2016). Consequently,
wenow examine the formative use of rubrics as a specic example of
how AI can address existing challenges, provide additional
opportunities, and present new challenges to this formative
assessment practice.
Firstly, AI can support the formative uses of rubrics by helping
teachers to overcome some of the time needed to construct rubrics
and to teach students how to use them, as these have been found to
bea constraint to rubric use (English etal., 2022). Generative AI can
speed up rubric design, as teachers can use it to provide samples of
rubrics to assess specied constructs, and a teacher can choose to
directly use these rubrics or to use them as a source of ideas for
designing their own. AI also has the potential to assist students as they
learn to use rubrics by providing work samples matching dierent
levels on a rubric, by assessing student-generated work samples
against a rubric so a student can check the accuracy of self-
assessments, and by providing written feedback to accompany a rubric
assessment. ese possible AI-augmented rubric uses by students help
build agency, as the students can have more control over the timing
and style of feedback they receive. erefore, AI has the potential to
help teachers overcome some of the common challenges to using
rubrics in the classroom.
Nonetheless, the challenges presented by potential biases in
training data are also applicable to rubrics generated with AI (Li etal.,
2023). Rubrics for constructs with a greater cultural inuence, such as
communication, are likely to be more aected than those for
constructs where the subject matter is more consistent irrespective of
culture, like chemistry. In addition, while there is an acknowledged
need for more research on rubric design (e.g., English etal., 2022), the
ndings of such research oen fail to be commonly adopted by
teachers. One example of this is that most rubrics have structurally
aligned categories, e.g., all parts of the rubric have ve levels of quality.
Unfortunately, there is empirical evidence that this common structure
is ineective because it increases construct-irrelevant variance by
facilitating scoring based on a halo eect where the assessor makes a
global judgment of quality and simply aligns the ratings on dierent
criteria of the rubric to match rather than making independent
decisions for each (Humphry and Heldsinger, 2014). Rubrics, thus,
support more accurate judgments when the number of levels of
quality are tailored to the specic criterion being evaluated where
some criterion (e.g., quality of argumentation) have more levels than
others (e.g., use of paragraphs). Consequently, without careful
curation of training data sets to ensure they meet best practice in
rubric design, AI generated rubrics will likely propagate common
design aws. Moreover, exploration by researchers of the dierent
ways AI is already augmenting rubric use in classrooms is warranted,
especially in cultures and contexts that are not well represented in
training data sets.
The role of self-regulated learning and
critical thinking in formative uses of AI
Based on the formative assessment cycle in Ruiz-Primo and
Brookhart (2018), there is a natural bridge between self-regulated
learning and formative assessment, as formative assessment can
beconsidered as a self- and co-regulated process of improving
learning, which starts with defining and sharing learning goals
and then through a process of gathering or eliciting information,
analysing and interpreting the collected information, and finally
using the collected information to make a reflective judgment on
whether the pre-defined learning goal has been achieved or not.
There has been a call for linking the research into self-regulation
with formative assessment, as it is recognized that self-regulation
will enhance students’ ability to act as peer-assessors, do self-
assessment and take on the proactive role needed for formative
assessment practices (Brandmo etal., 2020). Despite decades of
educational research into what improves students’ learning, few
researchers have tried to combine the two fields of formative
assessment and self-regulation, although exceptions include Allal
(2010), Andrade and Brookhart (2016), Brown (2018), Butler and
Winne (1995), Nicol and MacFarlane-Dick (2006), and Panadero
etal. (2018). Moreover, more recent research has demonstrated
how students can benefit more from formative assessment
practices if they are self-regulated learners (Allal, 2020; Andrade
and Brookhart, 2020; Perry etal., 2020). With the rise of AI,
students’ ability to self-regulate will beeven more important, as
it opens opportunities but also challenges in how weplan, use
strategies, and evaluate our learning processes.
Furthermore, weargue for the importance of critical thinking for
both teachers and learners to navigate the principled use of AI and
leverage the eectiveness of formative assessment as part of their
process of self-regulated learning, particularly when confronted with
the novel challenges of AI. Although highlighting the importance of
critical thinking may seem like an already labored point in educational
settings, as it has been acknowledged as a fundamental generic skill
necessary for individuals to live and thrive in the 21st century (e.g.,
Davies and Barnett, 2015), none of the existing research has yet built
a connection between critical thinking, formative assessment and self-
regulated learning under the impact of AI. Before getting into the
specic argument on the role of critical thinking in formative
assessment and self-regulated learning, it is worth clarifying that self-
regulated learning is used in a broader way in this section, extending
beyond learners to encompass teachers who also need to apply their
self-regulated learning skills to eectively acquire new knowledge and
skills to harness the potential of AI in their teaching practice
eectively. In the following part, we will briey explain our
understanding of critical thinking, depicting the role of critical
thinking when facing new challenges brought by AI, and then

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Frontiers in Education 06 frontiersin.org
describing the role of critical thinking in formative assessment and
self-regulated learning.
When facing the uncertain, complex issues brought by the
advancement of AI, critical thinking, dened as “reasonable and
reective thinking focused on deciding what to believe or do”
(Ennis etal., 2005: 1), becomes increasingly pronounced. As a
complex competency, good critical thinking practitioners are
expected to have a sophisticated level of epistemic beliefs (i.e.,
attitudes to knowledge and knowing), which are essential to
recognizing the uncertainty and complexity of such controversial
issues as the ethical use of AI in education. ese beliefs lay a
foundation for the engagement of their thinking skills (e.g.,
understanding, applying, analysing, synthesizing, evaluating) to
bewell-informed of the issue and navigate through a vast amount
of potentially conicting information (King and Kitchener, 1994;
Kuhn and Weinstock, 2004; Wertz, 2019; Sun, 2021, 2023). As
theorized by Dewey (1910), suspending judgments may bethe
most eective course of action prior to acquiring a comprehensive
understanding of a relevant issue.
For dierent responses on the issue of whether AI should beused
in educational settings, it is not surprising to witness resistance toward
emerging technologies because there is a natural fear and unease that
oen accompanies the introduction of new technologies (Ball and
Holland, 2009). However, if critical thinking is engaged before
deciding what to believe or do, this natural tendency can bechallenged.
AI is far from a novel concept and has been an academic discipline
since the 1950s (Haenlein and Kaplan, 2019; Gillani et al., 2023), and
various AI technologies (e.g., image recognition, smart speakers, self-
driving cars) and models (e.g., AlphaGo, Deep Blue and ELIZA) have
already signicantly impacted our ways of living and working
(Haenlein and Kaplan, 2019). Yet, limited transformation has
happened in education, with 20th-century traditions and practices
still dominating our schools (Luckin and Holmes, 2016). Despite the
recognition of the enormous benets and potential of AI in
transforming education, scholars’ impatience is mounting because
many of these promising ideas remain conned to the lab or lecture
halls with few practical breakthroughs (Luckin and Holmes, 2016).
Specic to the context of formative assessment and self-
regulated learning, the role of critical thinking is also pivotal,
which equips both teachers and learners to eectively address
emerging challenges. For teachers, as the landscape evolves with a
growing array of AI products and assessment data, being ooded
by abundant online learning resources can be overwhelming
(Schwartz, 2020). It would beincreasingly important for teachers
to critically evaluate what, when, and how to utilize these resources
to enhance their teaching methodologies and bolster student
learning. When facing an increasing amount of data that has been
collected or needs to be collected, teachers need to critically
discern how assessment data can best inform their pedagogical
strategies rather than have data to dictate their teaching.
Additionally, teachers should exercise discernment in determining
the level of trust they can place in specic AI models when making
judgments about student learning outcomes. is becomes
especially crucial for teachers who should critically assess the
potential biases that AI models might carry due to the use of
training data (Li et al., 2022, 2023). Furthermore, as AI
advancements have the potential to liberate teachers from routine
and time-consuming tasks like assignment grading and rubric
development, they must engage in critical reection. ey need to
consider which skills they should prioritize for their professional
development, such as data literacy, and what skills should remain
at the core of their teaching, notably critical and creative thinking.
is critical assessment of their evolving role is essential in
navigating the transformative impact of AI in education.
Regarding individual learners, engaging critical thinking can
have positive contribution to the eectiveness of formative
assessment and self-regulated learning when facing the
opportunities and challenges introduced by the advancement of
AI. For instance, the advancement of AI, indeed, can certainly
beused to generate text to pass the assessment of a subject, but if
learners are practicing their critical thinking and self-regulated
learning skills, they may ask themselves some reective questions,
such as what is the purpose of learning? Will a certain way of using
AI contribute to achieving their learning goals? When specic
solutions have not been produced to address the new challenges
brought by AI, individual learner’s practice of their critical
thinking and self-regulated learning may contribute to the ethical
use of new technologies. Despite some instances of learners
exploiting AI to evade plagiarism detection systems, it is
encouraging to learn from recent empirical research that many
students genuinely benet from the timely feedback and
companionship provided by AI (Skeat and Ziebell, 2023).
Moreover, these students display ethical awareness, being cautious
and mindful of their AI usage even in the absence of well-
developed regulations governing AI in education.
While some scholars have suggested that assessment is holding us
back from transforming our education systems (Luckin and Holmes,
2016, p.35), the advancement of AI may catalyze a “Renaissance in
Assessment” (Hill and Barber, 2014). Although the acceptance of AI
may encounter some resistance, the power of new technologies, if
unleashed with principled and research-driven use, may signicantly
change and improve ways of teaching and learning. In this vein,
Australian educational policymakers made a signicant shi by
granting permission for the use of ChatGPT and generative AI in all
government schools. is change followed the release of the Australian
Framework for Generative Articial Intelligence in Schools. It is
encouraging to observe the transition from a policy that limited the
use of ChatGPT across every Australian state and territory, except
South Australia, to a more welcoming and adaptable stance.
Conclusion
As wehave outlined in this article, despite decades of research on
formative assessment practices, teachers still face several challenges in
implementing these practices on a large scale. e use of AI in
classrooms has the potential for supporting formative assessment
practices, although we will argue, it will require some careful
considerations. Based upon what wehave outlined in this article,
wewill conclude with the following suggestions on how to integrate
AI into formative assessment:
1. Utilize AI for feedback assistance, particularly in large classes
where teachers struggle to give timely feedback to all students.
2. Promote self-regulating skills as students will need to take even
more responsibility for their own learning, when using AI. is

Hopfenbeck et al. 10.3389/feduc.2023.1270700
Frontiers in Education 07 frontiersin.org
includes goal-setting, monitoring progress, and adjusting their
study strategies based upon AI feedback.
3. Emphasize the ethical use of AI in formative assessment and
discuss the importance of integrity and responsible use of AI
tools, to avoid inappropriate uses.
4. Emphasize the role of teachers in guiding the students’ use of
AI. Teacher can help students interpret AI feedback, set
learning goals, and make informed decisions based upon
AI recommendations.
5. Encourage collaborative research between educators and
researchers to explore the eectiveness of AI in formative
assessment. Co-design studies with teachers and students to
assess how AI impacts learning outcomes and
student engagement.
6. Recognize the evolving role of teachers and facilitators of
AI-enhanced learning.
In the changing times of AI, students more than ever need
teachers to guide them using AI, and as researchers, we would
encourage colleagues to take part in co-designing studies with teachers
and students, where wetogether examine how to improve students
learning through formative assessment practices, critical thinking,
self-regulated learning and AI.
Data availability statement
e original contributions presented in the study are included in
the article/supplementary material, further inquiries can bedirected
to the corresponding author.
Author contributions
TH: Conceptualization, Funding acquisition, Project
administration, Supervision, Writing – original dra. ZZ:
Conceptualization, Writing – original dra. SS: Conceptualization,
Writing – original dra, Writing – review & editing. PR: Writing –
original dra, Writing – review & editing. JM: Conceptualization,
Writing – review & editing.
Funding
e author(s) declare that no nancial support was received for
the research, authorship, and/or publication of this article.
Conflict of interest
e authors declare that the research was conducted in the
absence of any commercial or nancial relationships that could
beconstrued as a potential conict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their aliated organizations,
or those of the publisher, the editors and the reviewers. Any product
that may be evaluated in this article, or claim that may be made by its
manufacturer, is not guaranteed or endorsed by the publisher.
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