An exploration of the effects of cognitive AI tutors towards childhood development

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Carnegie Mellon University
Artificial Intelligence and Agency in the Classroom
An exploration of the effects of cognitive AI tutors towards childhood development
Caitlin Huang, Yenlin Kuo, JiWoong Jang, Kayla Leung
{caitlinh, yenlink, jiwoongj, kyleung} @andrew.cmu.edu
66-304 Dietrich College Grand Challenge Research Seminar
Illah Nourbakhsh, Jennifer Keating
August 9th, 2019

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Carnegie Mellon University 0
Abstract 3
Introduction 4
Key Terminology 5
Educational Technology 5
Cognitive Tutors 5
Artificial Intelligence 6
Education Value System 6
History of Technology in the Classroom 8
Landscape Model for Understanding the Space of Possibilities 9
Proposed Model Axes 11
Personalization Axis 11
Agency Axis 13
Proposed Model Quadrants 16
Quadrant 1: Low Agency and Standardization 16
Quadrant 2: High Agency and Standardization 18
Quadrant 3: Low Agency and Personalization 20
Quadrant 4: High Agency and Personalization 22
Guidelines 25
Critique of Current Systems 26
Trust 26
Imbalance of Power 27
Timing and Monitoring 27
Information Gap 28
Locus of Control 29
Identity 31
Indifference Towards Material 31
Diminished Cultural Identity 32
Loss of Confidence 32
Solutions and Recommendations 33
Avoid Information Gaps 33
Encourage Open-Ended Approaches 34

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Provide Students a Point of Reference 35
Deprioritize Time Constraints 35
Conclusion 36
Acknowledgements 38
References 39
Bibliography 42

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Abstract
It is increasingly the case that scalable AI-based educational technology is being
incorporated into the American classroom due to negative pressures from political and
socioeconomic forces. We examine how such technology might change the classroom, by
considering the values that the American educational system seeks to engender, as well as
observing the historical use of technology in education. We recognize the role that agency has in
the educational space, and explore the possible implications that AI-based educational
technology may have on agency, and partition our analysis into the topics of student agency and
curricular personalization, representing differing manifestations of agency within the American
educational system. This forms the basis of an educational landscape​ ​model, where the
conjunction of high or low agency and personalization can be used to examine current
educational environments and determine how they fall into four distinct states. We observe that
the status quo in American education is at low agency and low personalization, while current AI
based education technology has the potential to change the state to one of low agency and high
personalization. Additional challenges remain to reach an ideal state of high agency and
personalization, which is illustrated in a case analysis of the Lynnette platform. We present
suggestions for improvement for existing systems similar to Lynnette such that the development
of future educational technology and the interactions between educators, students, and AI
systems are more closely aligned towards promoting agency and personalization.

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Introduction
Since the passing of the Elementary and Secondary Education Act in 1965, and its
effective reauthorization and redoubling in the form of the No Child Left Behind Act in 2001,
there has been significant focus in the US on improving the state of primary and secondary
education outcomes. More recent efforts, including the advent of Common Core standards, and
their subsequent instantiation via federal grants by Race To The Top, have focused on shoring up
educational standards nationally to close the performance gap with students from other
Organization for Economic Cooperation and Development (OECD) countries. Yet despite these
efforts, students from the United States rank in the bottom third relative to their cohort from the
OECD in tests of math, science, reading, and problem solving. In the case of students of minority
and socioeconomically disadvantaged backgrounds, these students perform markedly worse than
the OECD average (NGA et al 14).
The need for improved educational results comes at a time when there is continued
political pressure both at the federal and state levels to decrease public funding and access for
public education (Leachman et al). This, combined with the increasing gap between teacher pay
relative to the growing economy has created negative pressure on teacher recruitment (Sutcher et
al. 2). Thus, present realities have made it imperative to explore educational models and methods
which have the potential for significant cost-savings and improvements to access, while
maintaining the ethos of education so as to equip students with the skills and means to navigate
the economic and sociopolitical obstacles of the future. Automated technology, specifically
involving Artificial Intelligence (AI), is a candidate to become an effective tool to supplement

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traditional educational methods. With the increasing development and prominence of AI
technology, we believe it to be a valuable endeavor to examine the ways in which AI technology
is introduced into the classroom such that it not only improves young students’ mastery of
content, but also their development of critical thinking skills, self-motivated learning, and other
early education goals. At the same time, we aim to ascertain the effects that deeper
implementation and integration of technology in the educational system will have on teachers,
administrators, and the ethos of education.
Key Terminology
Educational Technology
There are many definitions for educational technology (EdTech). For the purpose of our
paper, we choose to limit our definition to a purely physical context. That is, educational
technology encompasses the physical devices used for facilitating or enhancing educational
instruction. Historically these devices would encompass tools such as the Internet, laptops, or
calculators, but in the present, they now include cognitive tutors, gamified learning systems,
online courses, grading software, and performance monitoring systems.
Cognitive Tutors
A digital educational platform or tool that is distinguished by its feedback while a student
learns, which may manifest in intelligent system adjusted content, practice questions, or hints
about the problem or concept at hand.

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Artificial Intelligence
AI, in the context of education, is the capability for computer systems to interact with its
environment through visual perception, speech recognition, and independent decision-making.
These systems typically make recommendations, regarding the learning material or how long a
student should study, based on their inputs and a student’s performance on previous tasks.
Education Value System
In order to provide a background for how new AI technology might affect the classroom,
we begin by determining the primary focuses of the American education system. The current
education system has three separate goals: promoting social efficiency, social mobility, and
democratic equality. Both social efficiency and social mobility are related to developing
students’ abilities in order to ensure that students either obtain the competencies necessary for
the workforce or acquire advantages that set them apart from others (Labaree 5). Following the
implementation of the No Child Left Behind legislation, the education system’s focus on
developing student abilities has emerged as the emphasis on educators to promote students’
knowledge acquisition and use standardized testing as indicators for students’ academic success
(Au 29). However, the emphasis on promoting social efficiency and social mobility comes at the
cost of promoting democratic equality, or the development of students’ belief in democratic
systems such that they participate in society as active citizens as students are slower to develop
qualities of civic virtue, such as empathy, integrity, and autonomy (Labaree 29).

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In our paper, we recognize that the current US education system prioritizes the two
aspects of social efficiency and social mobility through content mastery. However, we also
believe that promoting democractic equality is worthwhile, as attributes of civic virtue, such as
empathy, integrity, and autonomy also impact the abilities of students to acquire knowledge and
develop skill sets. Empathy affects the ease with which students learn as it relates to the
tendencies of students to reject new knowledge due to conflicting personal beliefs, while
integrity is related to the methods in which students acquire knowledge and reduces the
likelihood for students to fake content mastery. Autonomy is also related to student motivation as
it affects whether students are self-driven and capable of knowledge acquisition outside of the
educational setting. When we consider the methods through which students develop empathy,
integrity, and autonomy, we see that these qualities can be encompassed under more general
ideas of agency and personalization that determine whether students can exert control over their
learning experiences and the degree to which that experience is tailored to the student.
When we consider how the goals of the education system are present in current
educational technologies, we believe current technologies prioritize social efficiency and social
mobility. In cases such as cognitive tutors we note the promotion of knowledge acquisition
through the continuous practice of problems and the expected effects of improving students’
performance on standardized tests. As aforementioned, this emphasis comes at the cost of
democratic equality and civic virtue through agency and personalization. As such, by discussing
agency and personalization in the classroom, we hope to promote not only the educational goals
of social efficiency and social mobility, but also democratic equality with the use of new
technological tools such as AI systems.

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History of Technology in the Classroom
To provide a frame of reference for our discussion of how new AI technology might
interact and be implemented in the classroom, we first aim to orient ourselves by considering the
history of technology in the classroom and how particularly technologies have worked towards
providing an enhanced educational experience. The first notable piece of tech introduced to the
classroom dates back to 1923 when radios were first used in the classroom. Major cities
established classroom instruction on the radios that allowed students from around the city to tune
into class from afar. Not long after, overhead projectors that were originally used for US military
training purposes began to quickly spread to schools providing a shared visual learning
experience for students that quickly developed to also be accompanied by audio via sound films
and audiotapes. The next major milestone was in 1975, which marked the historic development
of the personal computer and the rapid advancement in technology that led to the development of
the Apple Macintosh for students. Following its release, the ratio of computers to students in US
schools was about 1 to 92. It was not until 2009 when this ratio lowered to 1 to 3, and computers
became a mainstream staple in the classroom, which was quickly followed by the introduction of
wireless tablets and interactable whiteboards (Educator Technology).
One notable observation of past technologies is that they have predominantly been
centered around promoting a shared enhanced education experience as with the overhead
projector, radio broadcasts, silent films, or even SMART boards. However, the recent movement
towards personal devices and personal computers has changed the educational focus away from
shared learning environments, instead moving towards a supplemental but isolated student

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experience with computer systems.These tools are increasingly useful supplemental applications
that aid teachers in the classroom, especially in instances where teachers are unable to devote
extended periods of one-on-one instruction time with students. At our current rate of
technological development, it seems likely that it will only be a matter of time before we see
Artificial Intelligence as commonplace technology in the classroom. So, as we stand at the
crossroads of another form of technology making its way into the educational landscape, it is
important to question what potential side effects on student growth and personal development
may arise and how exactly we might envision guiding the development of these technologies and
their application towards improving the greater educational experience (Educator Technology).
Landscape Model for Understanding the Space of Possibilities
Based on our chosen education value system, we focus on two key ideas of the
educational experience: personalization and agency. Specifically, we explore personalization
because there have historically been measures to make education systems within the classroom
more standardized. For instance, as an effort to collectively improve the academic performance
of US students relative to their international counterparts, the Common Core initiatives created
baseline standards for students to ensure that all students would have the skills needed to succeed
after secondary education. These standards try to align with the expectations of colleges and
employers. However, this alignment is not possible for all students, since each student’s
aspirations and interests differ greatly. Common Core’s broad standards target the average
student, who may not be an accurate representation of many other students. Therefore, there is
little room for students to investigate their own topics of interest and work towards their

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individual goals. Due to evidence that one-on-one tutoring can improve student performance,
cognitive tutoring systems have been developed to address the demand for personalized attention
towards each individual student’s learning profile (Bloom 7).
Additionally, we consider agency to be another essential component of our educational
model since affording more agency to students can determine their motivation to learn, which
allows students to better apply and understand their knowledge in the long term. Students can
tailor content to account for their own interests and determine how they interact with content,
which affords them varying levels of agency. Through our model, we examine how the
combination of different levels of personalization and agency can impact students and consider
examples of current systems that fall under these categories (Figure 1). We create an educational
landscape model that has two continuums: low agency to high agency and standardization versus
personalization. The continuous axes provide four quadrants, which create four categories of
possibilities that arise related to implementing educational tools.

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Figure 1.
Proposed Model Axes
Personalization Axis
There is increasingly an awareness of the need for educational content presented to
students to be personalized or adapted such that students are more likely to identify and associate
learning material with their life experiences. This is in contrast with the current state of affairs,
epitomized by the Common Core standard, espousing a consistent set of guidelines for what each
student should know in English Language Arts and mathematics at the end of each grade.
Common Core allows schools to measure their students’ performance and motivate changes in
the curriculum to meet the standards. However, the framework that curricula such as the
Common Core provide may not be appropriate for everyone, because it may not present themes

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best guidelines for students who have disabilities, are moving to a new school, or are learning
English as a second language, among others. Students who present knowledge gaps may not be
able to detect and address them in time in a classroom setting with a standardized curriculum. As
they progress in their education, these content area weaknesses may become more apparent.
The benefits of personalization are apparent in Benjamin Bloom’s two sigma problem,
which refers to an educational phenomenon that indicates that the average student tutored
one-on-one performs two standard deviations better than students learning through conventional
classroom instruction, on average (7). During one-on-one tutoring, personal tutors can adjust to
the student’s learning style and preferences and can find and help students with specific areas
they may be struggling in, before these weaknesses further interfere with a student’s progress.
However, since one-on-one tutoring is a resource that is often not physically or financially
accessible to students, we look to other means of personalization.
In general, personalization allows the learning content not only to best address a student’s
weaknesses so they can efficiently study, but also allows the material to relate more closely to
someone’s background. This can help people understand their world better and refine their
interests and sense of identity, which would be difficult to achieve from a standardized
curriculum (Gómez et al. 48). By relating material more directly to individuals, students may be
more engaged in fields that they otherwise would not be as interested in. In contrast, teaching
students who are uninterested in the material is difficult. According to a National Research
Council Study, students have preconceptions about how the world works, and if their original
viewpoint is not engaged, they may fail to understand new concepts. (Donovan et al. 20).
Without engagement, students may be lost once the material is presented, and as the information

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builds in complexity, students may be even less likely to gain enthusiasm for the subject.
Therefore, to address knowledge gaps that students may have accumulated, dynamic tutoring
systems can help a student rebuild his or her foundation by giving personalized feedback on
areas that students struggle in and reviewing these areas. With AI’s increasing ability to
personalize towards individual users, EdTech systems can become particularly well suited
towards this goal.
Agency Axis
Our current education system is predominantly focused on teaching to the test and has
associated successful student learning with the belief that students will pass standardized tests
that emphasize basic knowledge skills required by the Common Core. Most educational tools in
turn reflect this same goal in their design and marketing, claiming to be the system best suited to
increase student learning by a statistically quantifiable thresholds observable in the student’s test
results. This has almost all but removed conversations about the student’s experience when
exposed to new AI systems, such as cognitive tutors, and what unintended side effects may arise
from these interactions. Agency, derived from the Latin noun “agentem” meaning "to set in
motion, drive forward; to do, perform, an idea of little focus in many traditional classroom
settings is a powerful motivator of values that we seek to promote through the educational
experience, such as self-motivated learning and the confidence to problem solve in new
environments (OED). As a result, we are specifically interested in investigating how integrating
AI systems into the classroom environment might lead to an inadvertent change in a student’s

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sensation and perception of agency. In order to observe these changes, we begin by considering
how high and low levels of agency traditionally affect a student’s learning experience.
Agency is tied to the theory of constructivism, where development of understanding
requires children to experiment with and learn from the world around them, rather than through
structured instruction (​Ultanir 201​). Anthropologists and psychologists have shown that high
agency is critical to children's learning processes because children learn through observation,
participation, and initiative and through having the space and time to experiment with their own
ideas (​Adair​ 228). Examples of children using agency to exert control over their learning
experiences and expand their capabilities include “helping determine unit topics, experiment and
engage in open-ended exploration and conversation, and planning projects or help their friends
with ideas” (​Adair​ 227). By allowing students to explore materials and text, to generate new
content and questions, and to use their curiosity as motivation and inspiration for inventing,
planning, designing, and problem solving we would be more directly achieving the educational
value of developing self motivated learning. In particular, a case study conducted by Jennifer
Adair​ stresses these ideas with an anecdote of a young first grade student Mary, who after
becoming enraptured by the concept of a volcano, made deliberate self-determined efforts to
pursue an extensive investigation and experimentation into how one might simulate the
explosion of a volcano in a classroom (220). Not only was Mary able to pursue her particular
fascination for volcanoes, but through the experience she was able to engage with and explain to
other fellow classmates the processes of a volcano explosion.
Agency is also critical to a child’s development of autonomy and identity as children
explore the world through a lens crafted by their prior experiences. In the context of education,

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this development is present in children’s formation of their future selves as they discover their
academic interests and develop educational trajectories (Klemencic, 8). Relating to a student’s
identity can also increase children’s engagement in education and promote intentional
interactions by providing children with a belief that they are empowered and the decisions they
make matter (Lier 60). A study conducted by Ron Eglash and Audrey Bennet, highlights these
effects through an enthonomathmatic case study tying African American hairstyle to the teaching
of geometry. Their study cited the effects of a sense of content creation on improving students’
engagement in activities and their understanding on the relationships between being content
producers and content consumers. Additionally following the project, the study noted the
students’ development of identity as they strengthened ties to their cultural heritage and their
discovery of academic interests in mathematics, geography, and engineering (12).
In the spaces of low agency, students receive little control over their decisions, which
leads them to believe that they hold no stake in the education system. With little control over
learning experiences, students may become unmotivated and uninspired, which potentially leads
to a failure to acquire a love of learning as they will have lost many opportunities to explore and
discover their academic interests and passions. These issues are further compounded, in school
systems that lack resources and funding. Low student agency may result in low student
engagement, which can necessitate higher amounts of educator intervention that these school
systems are unable to provide. Additionally, the inability to pursue interests and a lack of control
may have ramifications beyond a student’s education. In particular, reduced student agency may
lead to ill-defined self identities or slow personal development that hinder the students ability to
problem solve, set goals, and operate autonomously outside both the supervision of a teacher and

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the structure of the classroom. As such, while students with reduced agency might continue to
succeed in skill based assessments, they may struggle to apply their skills outside of the
classroom (Klemencic 7).
Proposed Model Quadrants
Quadrant 1: Low Agency and Standardization
This quadrant represents an intersection of standardization at a curricular and educational
instruction level with a lack of choice for the students to progress through their education at their
desired pace; this state represents a curricular-driven system, rather than a student-driven one.
The most common realization of this, is an expectation for students to achieve certain
educational milestones within a specified time frame with respect to their assigned grade level.
This approach is one that dominates the educational field today and is most evident through the
implementation of Common Core standards across the participating forty-five states.
The benefits to this approach is in its ostensible simplicity -- setting a clear minimum
threshold for educational systems and educators leaves no ambiguity as to the objective from one
grade level to the next. Additionally, standardization of educational techniques and material
lowers the administrative burden of teachers. Yet, the convergence of standardization and low
agency necessarily represents a compromise from the ideals of education. Standards chosen often
will not represent realistic goals for the student populace as a whole, necessitating the creation of
an educational sequence which expedites learning at an unrealistic pace. Creating standards
which aim for the average US student to meet the performance of students from the most high

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performing educational system begs the question of how to meet such standards without the
considerable downsides of replicating those educational approaches. The emphasis of
educational standards on skills-based learning, which is easier to demonstrate in objective
metrics than development in critical-thinking skills, deprives students the opportunity to explore
topics of interest at a higher level. Standardization also fails to account for the heterogeneity of
socioeconomic and sociopolitical backgrounds that students may come from. Particularly in the
case of socioeconomically disadvantaged and disabled students, there is a lack of a robust
remedial or adaptive educational process to decrease the achievement and learning deficit these
students may have prior to entering the public education system. In the case of high-achieving
students, they may not have the resources necessary to challenge the boundaries of the
curriculum and therefore become less engaged.
The Common Core, as the new standard of the convergence of low agency and
standardization, has yet to be appraised in any statistically significant way, though early signs do
not offer a positive outlook. Researchers from the American Institutes for Research compared
test scores across the Common Core tested subjects (reading, writing, math) for states whose
standards were deemed significantly different than the Common Core, vis a vis scores from
students whose standards were closer aligned with Common Core standards prior to their
adoption. The primary observations were small negative effects on fourth and eighth grade
reading scores, with other metrics proving statistically inconclusive. The researchers worry,
however, that the negative effects will grow over time, given that the effects of the standards
change will accumulate (Song et al. 4).

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Quadrant 2: High Agency and Standardization
Current public school systems have adopted the Common Core at the state and local
levels, as a means of evaluating student development and determining whether students have
acquired certain skill sets recommended for their grade levels. Standardized testing was thus
implemented as a means of determining student proficiency as it provided an avenue through
which educators could determine student weaknesses and strengths. However, its implementation
and its utilization in determining school funding has also led to side effects associating test
results with school quality and an emphasis on the accountability of educators and school
systems which report low or failing test scores (William 8). Due to the large number of content
standards required for student proficiency and limited resources, educators have restructured
courses and classes towards satisfying the minimum baselines necessary for students to pass the
standardized assessments. Part of this restructuring has led to an emphasis on test preparation
through practice and worksheets, that limit the time and ability for students to explore academic
interests as well as a reliance on standardized test results, that are perceived by students as
barriers blocking educational learning.
A case study conducted by Laura-Lee Kearns that investigated the perspective of students
following their exposure to the Ontario Secondary School Literacy Test, highlights these
concerns. When asked about their experience with the assessment, students noted the
discomforting notion that their success was being determined by an apathetic faceless test grader
rather than the supportive and encouraging teachers whom they engaged with day to day. Upon
being informed that they had failed the state’s standards for literacy, many students voiced

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concerns that they now questioned their own competencies compared to their peers and had
become fearful that their failure would negatively affect their future aspirations. In extreme
cases, students voiced opinions that they believed that the test punitively destroyed their ability
to take desired courses or acted as a degrading mechanism that lowered their self esteem (126).
Despite the conflicting ideas between agency and standardization, there still exist cases
where standardization can allow for the potential for student choice. In the context of open-ended
instructional learning environments, students are given the ability to choose the methods in
which they learn, whether it is through a completion of a traditional worksheet or undertaking a
project related to the standardized content. In these environments students can also be given the
ability to determine whether they prefer one on one with an educator or group learning through
interaction with their peers. Student agency can also exist in the context of assessments, as some
educators have developed creative projects, as an alternative to standardized testing, that act as
measures for determining student proficiency. One such example is mentioned in a study
conducted by Robb Lindgren and Rudy McDaniel, where a digital media course was redesigned
to increase agency by allowing students to choose learning content related to the course topic as
well as design their own digital creation to show their proficiency in the course material. While
student agency was still limited in the sense that the content options were predetermined by the
educator, students still recognized that they had been given the ability to choose and felt more
empowered over their own learning (7).
However, providing high agency in a standardized setting can also become detrimental to
students’ learning processes. In particular, students who are given a choice to partake in the
standardized environment may lack the motivation to do so, resulting in them failing to work

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hard enough to achieve benefits from their education. An example of this issue would be with
Massive Open Online Courses (MOOCs) where students have complete control over their
involvement in an online curriculum. MOOCs were originally designed to increase access to
higher education by providing large scale instruction, which as a consequence led to a
standardized structure similar to traditional classrooms, but with the added benefit of allowing
students to learn at their own pace and choose subjects of interest. However, the completion rate
of MOOCs is thirteen percent, far lower than completion rates of similar courses in the
traditional setting (​Colchester et al. 50)​. As students are given complete control over their own
learning rather than having it dictated by an educator, students must rely on their own intrinsic
motivations in order to finish the course. This can be difficult, as the content of the course is
often generalized and lacking in personalization, which makes it hard for students to relate to the
course content and motivate themselves to engage with the material. As such, while cases of high
agency may exist in a standardized setting, the educational outcomes can be detrimental, due to
the lack of personalization that provides students the motivation necessary to take control of their
own learning.
Quadrant 3: Low Agency and Personalization
With the increasing prominence of AI technology, there is a subsequent increase in the
ability for learning AI systems to provide a personalizable experience for learners. This allows
for each student, rather than receiving identical cookie-cutter worksheets, to have the opportunity
to work through topics and exercises that target their problem areas, patterns of specific
mistakes, and knowledge gaps.

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Lynnette is an instance of one such intelligent tutoring system geared towards teaching
students to solve linear equations, by guiding students step-by-step to improve their equation
solving ability through appropriately prompting students with hints and feedback based on
analysis of their incorrectly entered input. Lynnette is also distinctly noted to stand out against
traditional problem prompting systems by providing personalization through “adaptively
selecting problems for each student, using Bayesian Knowledge Tracing to track individual
students’ knowledge growth, together with a mastery learning policy” (Holstein et al. 3).
However, while Lynnette tunes the problem sets towards a student’s particular aptitude and
proficiency and enables each student to work at their own pace, the student users of Lynnette
lack agency, due to it being limited to clicking for hints or for the next problem. Moreover, the
little agency students have to direct their own pace of learning and click through hints and new
problems is acutely monitored by a supplemental teacher monitoring system known as Lumilo.
Through a mixed reality visual experience, teachers using Lumilo as a companion system to
Lynnette receive “real-time indicators of students’ current learning, projected in the teacher’s
view of the classroom.” The tracking of every student action and mouse click or lack thereof is
relayed to the teacher through a wide range of metrics notifying them if students are idle and not
engaging with the tutoring system, spamming the problem with different wrong answers, or
abusing the hinting software to get at the answer. For each student, the teacher can know exactly
the problem that the student is currently working on, the number of hints they have used, the
time they've spent on the problem, past problems they've answered incorrectly, and their top
three weakest topical areas of struggle (Holstein et al. 5). These metrics keep students alert while
working through the system with the knowledge that they need to statistically measure up to the

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ideal performance level of answering a threshold of questions correctly or completing all their
problems within a designated time frame.
Such a monitoring system, despite its best efforts to understand areas of
misunderstanding a student may have and accordingly adjust the pace of questions, brings to
question the role of the student as a blind believer and follower of computerized directions. In
this particular instance, the introduction of an intelligent tutoring and monitoring system into the
classroom does not enable students to feel more empowered and self-directed in their learning
experience. The system may indeed direct the student’s mastery of a problem topic through
prompting for appropriate hints to aid the student, but the system is also prone to abuse by
students that game the system into believing that they have achieved skills mastery (Holstein et
al. 4). However, beyond an assessment of performance on whether the educational goal of
achieving skills is attained, there is no affordance for any student control and exploratory
freedom through such a stagnant AI learning system. Despite marketing claims that Lynnette and
other similar systems are personalized to the student, these tutoring systems ultimately
disimpower and diminish the student’s identity by removing their ability to ask open-ended
questions and experiment with different ideas, while introducing the looming sense of insecurity
from the continuous monitored achievement assessment by the systems.
Quadrant 4: High Agency and Personalization
When we combine agency with active personalization from the education system,
students who are allowed higher levels of agency can achieve greater gains in education. Agency
and personalization can allow students to be more autonomous, which promotes the education

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systems’ values of social efficiency, social mobility, and democratic equality as students are
more driven to developing individual abilities and acquiring knowledge that benefit themselves
and society.
People tend to be the most self-motivated and thoughtful when exploring topics that they
are most interested in and learn better when given more freedom to focus on their interests. For
example, in one study, when students were allowed to control the mouse while reading a
computer-based storybook, they expressed more interest in the task, were more attentive, and had
higher levels of comprehension than students who were given more explicit instructions by
adults (Calvert et al. 587). Students who read the book while an adult controlled the mouse were
found to have statistically significant decreases in attention in the later half of the task, while
students who controlled the mouse themselves did not. Those who had more control during the
task exercise agency, for their actions stemmed from self motivation to explore new topics,
rather than being decided by an adult. By choosing which interactive features of the book they
wanted to experiment with, such as which words they wanted to learn more about, they were able
to personalize their experience towards their interests and knowledge gaps and first-hand
experience the freedom of controlling their story.
Additionally, game-based learning, a category of game play that has set learning
outcomes, is another such system that engenders student agency by allowing students to focus on
their interests and sense of identity (Snow et al. 359). Effective game-based learning systems
draw students into virtual environments that are both familiar to the student and relevant to the
learning objectives. As students choose different roles when interacting with game-based
systems, they have more immediate exposure to the educational content because they identify

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with their role and develop the same goals as their character. Students work towards a goal,
making decisions and experiencing the consequences or benefits of those actions. The setting is
risk-free, so students are able to practice improving their skill sets, without fearing failure. This
improves student engagement, and through the objectives of the game, students may develop an
interest in the subject matter and want to continue to explore the topic in a natural way,
endowing them with a sense of agency.
Some personalized education systems not only take in consideration the understanding
that a student has of the subject matter, but also their motives for learning, user activity, and
observable user behavior. For example, the European research project 80Days created a
game-based learning environment that aims to engage students by creating a player model that
takes in consideration factors, such as their competitiveness, skills, and emotional state (​Göbel
et al. 11)​. In the game, the storyline and difficulty of the material adjusts based on the player’s
changing state. Users also exercised agency by controlling parts of the story structure. Players
enjoyed the motivating, suspenseful, and playful form of learning. By tailoring an educational
experience towards an individual, students can focus on their content weaknesses while not
losing confidence in their abilities. However, when combining personalization with agency,
students receive more control over the task at hand and are more engaged with the material
because they develop a greater sense of responsibility for their actions during the task.
As such, we identify that in this ideal educational space, cognitive tutoring systems
should use techniques to engage their students through personalization while still offering them
the power to make many choices, ensuring their sense of agency in the classroom. Unfortunately,
most current education systems lack either or both agency and personalization. As we have

26. 25
noted, all the other quadrants have shortcomings when it comes to either one of the features of
increasing engagement through personalization or prioritizing the student’s ability to direct their
learning options, or both.
Guidelines
Given our presented model, we identify that most modern AI classroom systems fall
under the space of having higher personalization but significantly diminished agency. Therefore,
we direct our analysis and recommendations within this section towards addressing how we can
move towards the ideal space of shared high agency and personalization from present day
educational space of low agency with some existing degree of AI personalization.
We choose to focus on agency because while many current education systems already
attempt to integrate personalization, their methods come at a cost to the students’ degree of
agency. To offer concrete and applicable recommendations, we examine the case of the cognitive
tutor Lynnette and its companion monitoring system Lumilo, previously introduced in Quadrant
3, as one such technological system that offers personalization but falls short when it comes to
promoting agency. We first aim to examine the problems of this system and ways in which it
lacks agency for students by further partitioning the concept of agency into three aspects: trust,
locus of control, and identity (Figure 2). We consider these three domains in order to guide our
description of the greater problem of agency with systems that reside in the realm of the low
agency space of our model, but we do not consider these three areas to be a fully exhaustive
breakdown of the idea of agency.

27. 26
Figure 2.
Critique of Current Systems
Trust
We consider two different definitions of trust to guide our analysis. One dimension of
trust depends on reliable and predictable patterns of behavior that generate a reasonable
expectation of a system. A second dimension of trust depends on fundamental understanding of
core values and beliefs that drive decisions and behaviors of another. This second notion of trust
is “in interpersonal relationships and dependencies… because the trustor has beliefs and
expectations about the trustee that extend beyond mere predictions about what the trustee will
do” (Danks and Roff 2).

28. 27
Imbalance of Power
In most classrooms, this issue of trust is oftentimes trivially dismissed as most students
assume some trust in the computer system since many of these educational tools follow a fairly
strict repeated pattern of prompting students with a question, offering a potential hint to help the
student, then explaining the solution whether or not the student answered right or wrong.
However, with such a repetitive prompting system, concerns arise as to who dictates the learning
interaction and experience as there appears to be an imbalance of power. When using Lynnette,
students are afforded little control but to click forward to the next problem they get assigned by
the system and required to complete ​(Holstein et al. 3)​. The expectation of the student is that they
blindly trust that the system is providing them the problems best suited for their learning and
skill development without explanation or interactive preface to demystify such systems. Lynnette
dictates what the student learns, how they should learn, and when they should be expected to
have learned it, removing the student’s sense of power entirely to direct their learning
experience.
Timing and Monitoring
The lack of student’s trust in the system is further exacerbated by a timing system, such
as the one that Lumilo implements to track the rate at which students are completing their given
problems. This system reports students for potentially gaming the system or being distracted
based predominantly by the pace at which students are working through the material against
some idealized average timing range that the system determines ​(Holstein et al. 4)​. Furthermore,
additional privacy and trust concerns arise because information about monitoring statistics are

29. 28
not disseminated. Thus, students are not readily aware of the background monitoring that occurs
when they are working through problems. Information about the student is only selectively
accessible. Rather than tracking transparently, the Lumilo system only reveals its results for a
point of comparison and measurement of success or failure, offering little possibility for trust in
this hidden monitoring system.
Information Gap
When considering the two definitions of trust it becomes simpler to see why students and
parents traditionally have such a high degree of comfort and trust in educators in the classroom
because these teachers exhibit the second dimension of trust by possessing core values of a love
of learning, teaching, and a desire to look out for the best interest of their students. For AI
systems, in particular, we struggle significantly more with ascribing the second definition of trust
because it is fundamentally challenging to relate on a values level with a man-made machine or
even an autonomous intelligent machine that is non-human. It becomes unclear if these core
teaching values that we would want endowed within the system should be designed by the
developer or inherent to the system and its intelligence. When we attempt to bring
personalization into the picture, it becomes more difficult to navigate what would constitute
repeatable patterns of behavior that promote trust and reasonable expectations when the system
could behave differently towards every student given different circumstances, collapsing
possibilities for even a first dimension of trust. Little to no explanation is offered as to how these
systems operate or reason through the different decisions that make for the student, and this
results in a degree of information asymmetry that makes the possibility of trust particularly
difficult.

30. 29
If a system is difficult to trust, then students and teachers will inherently interact with it
more conservatively because they are trying to fit the constraints of the system. Through
trust-driven interactions that aim to accommodate the system, the student eventually ends up
compromising their purpose of their interaction and overall agency.
Locus of Control
We define locus of control to be the degree to which administrators, students, and
teachers, believe they have control over the outcome of events in their lives and educational
experience, as opposed to external forces such as educators and AI systems that are beyond their
control.
Lynnette presents challenges to both students’ and teachers’ locus of control, which
threatens to break down the sense of agency that students and teachers may have through their
input into the educational process. From the student’s perspective, the fact that he or she is
constantly monitored, with little information as to how information gathering occurs, has the
potential to present uncertainty behind the motivation for the monitoring. Interaction with
cognitive tutor systems like Lynnette have the potential to be seen by students as akin to being
placed in a digital panopticon; ideally we would like to see that these educational tools be
recognized as a potential positive influence in the learning process. The constant monitoring,
coupled with the time pressures necessitated by curriculum-dictated rates of progress, creates
conditions where a student is liable to interpret learning from a cognitive tutor as an inorganic
experience dictated by external forces. In a similar vein, teachers’ experiences can be affected by
the educational material and methodology utilized by cognitive tutors, and depending on a

31. 30
teacher’s familiarity with the workings of the system, there is the possibility for teachers to feel a
lack of control in their efforts.
The manner in which cognitive tutors are used to adapt to subjects and how they accept
student responses have the potential to contribute to this feeling. A substantial number of
cognitive tutors used in education, including Lynnette, focus on subjects, such as math, which
lend themselves to student responses that are easily determinable to be right or wrong. In those
subjects, how students arrive at a correct or incorrect answer is often more important than the
answer itself, yet cognitive tutor systems appear to link aptitude with the amount of time spent
per question and proportion of correct answers as a proxy to mastery of mathematical concepts
and methods. This method is problematic because it associates skill acquisition and learning with
probabilistic correctness. Thus the system does not adapt to the degree as to which students
understand the topic at hand - the system is not capable of differentiating when a student reaches
an incorrect answer due to a minor or major misunderstanding.
At a macroscopic level, we see that Lynnette does not readily allow for control in the
scope of topics available to a student or teacher. Each subject is clearly delineated and broken
down into smaller sub-topics, which then have practice problems associated with it. This raises
the concern that a student’s avenue for exploration, particularly into topics and problems at the
intersection of subjects of the standard school curriculum is diminished. In the standard
classroom setting, the teacher has the ability to field questions from students about these topics,
but with present-day cognitive tutors, there is no method in which to do so.

32. 31
Identity
We define identity as an individual’s sense of self and the decisions he or she makes
based on his or her personal values. Research has shown that identity is important to a student’s
educational growth, for a student’s identity can affect their engagement in education, promote
intentional interactions with their peers and teachers, and facilitate both autonomy and self
motivated learning (Lier 60). When considering how Lynnette might affect identity, we focus on
the following aspects of identity:​ curiosity (how the student is engaged with Lumilo), culture
(the student’s perspective and experiences derived from interaction with Lumilo, opportunities to
develop and pursue academic interests), and confidence (how the student feels during the
interaction and whether they believe they are competent in the topics taught by Lynnette).
Indifference Towards Material
Lynnette and most other cognitive tutors teach and assess students by asking static
questions, which students frequently answer through a selection of multiple choice responses.
Educators often pre-design these static questions, which reinforce concepts that might appear on
a standardized test. As answering these questions compose the majority of student interactions
with the system, students may not feel engaged by the material, as it is not personalized.
The cognitive tutor’s emphasis on the topic at hand restricts the ability for students to explore
topic areas of interest. Since the performance of the student depends solely on their ability to
complete assigned problems, many students lose their curiosity as they are rationally driven to
only focus on topics related to the questions, rather than spending time exploring other subjects.

33. 32
The lack of curiosity potentially leads students to miss opportunities to discover academic
interests or passions, which may hinder self motivated learning outside of an educational setting.
Diminished Cultural Identity
Lynnette and other cognitive tutors may also limit a student’s sense of culture as when
cognitive tutors are applied outside of mathematics; content taught may be provided in a singular
method that limits students’ perspectives. For example, if the traditional static questioning
mechanic is applied to the field of history, students may focus on their ability to recall a
particular narrative decided by the designer, rather than the ability to engage in higher level
thinking through either discussion with peers or forming connections to personal experiences.
Another issue is that these systems are often directed towards a general audience, which means
they often apply the same learning techniques to each student, regardless of the students’
backgrounds. As such students may have poor interactions with a cognitive tutor, due to its
inability to accommodate individual experiences, that would have otherwise been recognized by
an educator in a traditional setting.
Loss of Confidence
Lynnette and other cognitive tutor systems are also often unable to determine the quality
of questions or the presence of alternative answers. In a traditional setting, students are able to
contest alternate solutions or poorly worded questions with a teacher, whereas with a cognitive
tutor, students may feel disempowered as they are forced to accept arbitrary decisions made by
the system. Lynnette is also unable to accommodate human factors, such as fatigue or frustration,
as it assesses students' performance based on their ability to correctly complete problems in a

34. 33
timely fashion. As such, students who fail to consistently answer questions correctly may feel
less confident and find themselves discouraged as they question their own abilities and
competency.
Solutions and Recommendations
In this section, we recommend ways to address the shortcomings of current EdTech
systems investigated in the previous section.
Avoid Information Gaps
Since the inner-workings of EdTech tools may be opaque to both teachers and students,
these tools can present themselves as mysterious “black box” systems, leading students and
teachers to hesitate to use them. Even if they overcome their hesitation, they may believe that the
decisions these tools make are more accurate than their own intuition because these systems are
seemingly complex and more sophisticated. Another possibility is that educators may not trust
the decisions that these systems make. However, EdTech tools are most reliable when they work
with teachers and students rather than dictating a course for students and teachers to passively
take. Providing prior information and an orientation of the workings of the tool, the ways in
which it interprets information and makes suggestions, for both students and teachers can
increase the trust in these systems. Users can understand how and why these systems make
decisions so that they can determine for themselves when to learn from these systems or when to
intervene since they see another possibility. Priming on how the system works will ideally
introduce the monitoring and evaluation systems, how they work, and explain their shortcomings

35. 34
such that students and teachers perceive a sense of control over the EdTech system. In order to
encourage a maximal sense of agency, we recommend that these systems continually reinforce
the notion that the education process should involve the active input of teachers and students.
Encourage Open-Ended Approaches
EdTech tools may also have many constraints when evaluating student responses, which
can restrict students' interactions. To address these limitations, students should be able to both
explain their answers and ask follow up or clarification questions to a teacher. For example, after
receiving a score on a task, students who believe they deserve a higher score or a better response
from the assessment system should have the opportunity to explain their reasoning to their
answers or seek out alternative explanations. When there is more flexibility and transparency in
the process of evaluation, students gain more knowledge from understanding the areas of
improvement for their responses and from thinking more insightfully about the prompt so that
they can learn more implications about the topic. We recommend that designers of EdTech tools
prioritize incorporating more free-response questions rather than multiple choice questions into
their learning systems because multiple choice questions can limit student’s knowledge of
concepts to the definitions provided in the “correct” answers rather than challenge students to
synthesize facts to understand and apply the material. Teachers also should be in a position of
power to make the final decision when evaluating student responses, understanding that these
systems have limitations. The EdTech platform should be designed from the ground up with the
idea of deferring ultimate decision making to the teacher to make available every opportunity for

36. 35
teachers to give input on student performance, including the opportunity to manually override the
software’s perception of a student’s performance.
Provide Students a Point of Reference
To address the problem of students not having many opportunities to apply the skills they
have learned to real-world scenarios, we suggest that teachers let students determine how they
would like the material to be applied. This would give students a frame of reference upon which
to motivate their knowledge acquisition, increase their engagement as they contributed to their
lesson, as well as give an opportunity to reinforce learning through application. Furthermore,
educational tools can incorporate a student’s culture into the lesson plans. Students can have a
sense of belonging in their classroom or learning environment, which can increase their sense of
familiarity and ownership. Providing a point of reference for the content they are learning can
further increase their confidence in their ability to learn and promote their agency in learning the
material.
Deprioritize Time Constraints
Even though time is a factor that many educators consider when planning, we suggest
that there be less strict time constraints to allow for curiosity and exploration. Oftentimes, when
students are timed, their primary focus may shift to completion instead of comprehension.
Although EdTech systems should not stress speed, distraction-free studying should still be
emphasized to encourage students to learn efficiently without rushing. Introducing and
emphasizing open-ended questions, and developing evaluation systems to analyze such content
in a time-pressure free context will help encourage a deeper understanding of the topic at hand.

37. 36
To ensure that students understand the objectives of the lessons and the questions, these systems
should prompt students to ask exploratory questions about problems. Additionally, many EdTech
systems give feedback to the instructor as to how the system believes the student is performing.
Implementations of such a system run the risk of connoting negative intent to a student’s actions
and creating a negative feedback loop for a student’s communication with the system or the
teacher. We see this with Lumilo’s alert that a student is “hint-spamming” which can negatively
prime the teacher into believing the student is lackadaisical in his or her work, rather than
realizing that such actions could have also resulted due to the student’s frustrations over the
effectiveness of the hint system (​Holstein et al. 4)​. We therefore recommend that the system
utilize intent-neutral language, such as “high frequency of hints used,” or statistical assessments
that defer any degree of judgement on performance, as to allow for teachers to reach their own
conclusions as to the context of such behavior.
Conclusion
In this paper, we examined two important aspects of education: personalization and
agency, and identified four spaces of possibilities in education based on the varying degrees of
personalization and agency afforded. We were particularly interested in the presence of agency
or lack thereof in the classroom and the impact of agency on a student’s ability to achieve the
educational value goals of promoting empathy, integrity, and autonomy among students to
ensure that they can use their skills they acquired to best benefit themselves and society. Our
review of the current educational system and cognitive tutor technology revealed that many of
these existing systems do not consider the ways in which they diminish student agency in

38. 37
classroom interactions. To better understand the nuances of interactions with technology that
diminish agency, we split agency into three aspects: trust, locus of control, and identity. By
exploring a case study in which the tutoring system Lynnette provides personalization but low
levels of agency, we delineated categorical problems with the system. Based on these identified
issues, we propose that there be increased communication between educators and designers of
EdTech to ensure that the interests and goals of the designers and educators align. Additionally,
we advocate for students and teachers to have an influence on the design of the system to ensure
that they are comfortable using the technology. In this research endeavor, we acknowledge that
AI technologies still have significant room for improvement, and that the US educational system,
built firmly on standardization, cannot be changed overnight with the use of a panacean
supplemental AI system. Nevertheless, we hope that this work guides future conversations and
research regarding how educators and EdTech developers may lead the charge towards
introducing more personalized and student-empowering technologies.

39. 38
Acknowledgements
We would like to thank Dietrich College for providing us this opportunity to partake in
the AI & Humanity Research Seminar this summer and for covering the program cost, enabling
us to gain a particularly fruitful experience working in a small research group.
We would also like to thank our faculty advisors, Jennifer Keating and Illah Nourbakhsh,
for guiding us throughout this entire research endeavor. From day one they helped us focus in on
our area of interest and articulate our thoughts and then consequently to narrow in on our goals
and presentation of materials. We are so grateful to have spent the last six weeks under their
mentorship and to have been able to learn from their experiences and anecdotes over tea and
blueberry scones. This paper would not have been possible without their support.

40. 39
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