Is Artificial Intelligence Good or Bad for Education

Artificial intelligence (AI) is no longer a futuristic idea confined to science fiction. In recent years, AI has moved rapidly into classrooms, lecture halls, and online learning platforms (source: https://education.illinois.edu/about/news-events/news/article/2024/10/24/ai-in-schools--pros-and-cons). From personalized tutoring systems and grading assistants to automated plagiarism detection and AI-driven chatbots, education is undergoing a profound transformation. But with these opportunities come significant challenges. As schools, universities, and policymakers grapple with this technological shift, the debate intensifies: Is artificial intelligence good or bad for education?

Many of the priorities for bettering teaching and learning are still unfulfilled today. Teachers look for safe, efficient, and scalable technology-enhanced methods to achieve these needs. Teachers naturally question whether the quick changes in technology in daily life could be beneficial. In their daily lives, educators utilize AI-powered services like voice assistants in their homes, tools that can create essays, correct grammar, and complete sentences, and automated travel planning on their phones, just like everyone else. Since AI tools have just been made available to the general public, many instructors are actively investigating them. Teachers see potential to expand the support provided to kids with disabilities, multilingual learners, and others who can benefit from more adaptability and personalization in digital learning tools by utilizing AI-powered features like speech recognition. They are investigating how AI may help them write or enhance lessons, as well as how they locate, choose, and modify content for their classes.

Teachers are also conscious of emerging dangers. Powerful, helpful features may also come with additional security and privacy dangers. Teachers are aware that AI is capable of automatically generating incorrect or improper output. They are concerned that unintended biases may be amplified by the associations or automations produced by AI. They have observed fresh ways in which pupils could pass off other people's work as their own. They are fully aware of pedagogical techniques and "teachable moments" that a human teacher can address but that AI models miss or misinterpret. They are concerned about the fairness of algorithmic recommendations. The worries of educators are numerous. It is the duty of everyone involved in education to use the positive aspects of AI integration in edtech to forward educational goals while simultaneously guarding against potential risks.

Source: https://ebookace.com/is-artificial-intelligence-good-or-bad-for-education/

1. IsArtificialIntelligenceGood
orBadforEducation?
Artificial intelligence (AI)is no longer a futuristic idea confined to science fiction.
In recent years, AI has moved rapidly into classrooms, lecture halls, and online
learning platforms(source:https://education.illinois.edu/about/news-
events/news/article/2024/10/24/ai-in-schools--pros-and-cons). From
personalized tutoring systems and grading assistants to automated plagiarism
detection and AI-driven chatbots, education is undergoing a profound
transformation. But with these opportunities come significant challenges. As
schools, universities, and policymakers grapple with this technological shift, the
debate intensifies: Is artificial intelligence good or bad for education?
Many of the priorities for bettering teaching and learning are still unfulfilled
today. Teachers look for safe, efficient, and scalable technology-enhanced
methods to achieve these needs. Teachers naturally question whether the quick
changes in technology in daily life could be beneficial. In their daily lives,
educators utilize AI-powered services like voice assistants in their homes, tools
that can create essays, correct grammar, and complete sentences, and automated
travel planning on their phones, just like everyone else. Since AI tools have just
been made available to the general public, many instructors are actively

2. investigating them. Teachers see potential to expand the support provided to
kids with disabilities, multilingual learners, and others who can benefit from more
adaptability and personalization in digital learning tools by utilizing AI-powered
features like speech recognition. They are investigating how AI may help them
write or enhance lessons, as well as how they locate, choose, and modify content
for their classes.
Teachers are also conscious of emerging dangers. Powerful, helpful features may
also come with additional security and privacy dangers. Teachers are aware that
AI is capable of automatically generating incorrect or improper output. They are
concerned that unintended biases may be amplified by the associations or
automations produced by AI. They have observed fresh ways in which pupils
could pass off other people's work as their own. They are fully aware of
pedagogical techniques and "teachable moments" that a human teacher can
address but that AI models miss or misinterpret. They are concerned about the
fairness of algorithmic recommendations. The worries of educators are
numerous. It is the duty of everyone involved in education to use the positive
aspects of AI integration in edtech to forward educational goals while
simultaneously guarding against potential risks.
Three arguments for addressing AI immediately were made by participants in the
listening sessions:
First, AI could make it possible to accomplish educational goals more effectively,
more cheaply, and at a larger scale (source:
https://www.faulkner.edu/news/the-future-of-learning-positive-applications-
of-ai-in-education/).AI may increase the adaptability of learning resources to
students' needs and strengths. Addressing the diverse incomplete learning of
students as a result of the epidemic is a policy priority. Enhancing teaching is a
top priority, and AI may help teachers more by way of automated assistants or
other technologies. When teachers run out of time, AI might also allow them to
continue providing support to specific kids. A top concern is creating resources
that are sensitive to the experiences and knowledge that students bring to their
education—their cultural and community assets—and artificial intelligence (AI)
may make it possible to better tailor curriculum materials to local requirements.
AI has the potential to improve educational services, as demonstrated by voice
assistants, navigation tools, shopping recommendations, essay-writing
capabilities, and other well-known uses.

3. Second, worry over possible future threats and awareness of system-level risks
give rise to urgency and importance. For instance, students might be more
closely watched. Although theU.S. Department of Educationadamantly denies
that AI might replace teachers, some educators are concerned that they might be
replaced. A voice recognition system that struggles with regional dialects or an
exam monitoring system that might unjustly flag some student groups for
disciplinary action are two examples of algorithmic bias-based discrimination that
the public is aware of. Some applications of AI might be opaque and
infrastructural, raising questions about trust and transparency. AI frequently
appears in novel applications with a sense of enchantment, but educators and
procurement regulations demand that edtech demonstrate effectiveness.
Artificial intelligence (AI) may produce information that seems true but is
erroneous or unfounded in reality. Above all, AI poses new risks beyond the well-
known ones related to data security and privacy, like the potential for pattern
detectors and automations to scale and cause "algorithmic discrimination" (i.e.,
systematic unfairness in the resources or learning opportunities recommended to
certain student populations).
Third, the magnitude of potential unforeseen or unintentional repercussions
creates urgency. Teachers may find unintended repercussions when AI allows for
the large-scale automation of educational decisions. As a basic illustration,
achievement inequalities may increase if AI adjusts by accelerating the curriculum
for some students and slowing it for others (based on insufficient information,
subpar ideas, or skewed presumptions about learning). The quality of the data
that is now accessible can occasionally lead to surprising outcomes. An AI-
powered teacher hiring system, for instance, would be thought to be more
impartial than one that scores resumes by hand. However, the AI system may
deprioritize applicants who could provide both talent and diversity to a school's
teaching staff if it is dependent on past data of low quality.
In conclusion, in order to take advantage of important potential, avoid and reduce
emerging hazards, and deal with unforeseen consequences, AI in education must
be addressed immediately.
According to theStanford Institute for Human-Centered AI's 2025 AI Index
Report(source:https://hai.stanford.edu/ai-index/2025-ai-index-report),there
has been a noticeable uptick in both AI investment and ethical research, including
studies on fairness and transparency.

4. Top Takeaways:
- New benchmarks introduced in 2023 (MMMU, GPQA, SWE-bench) saw large
performance gains in one year (e.g. +18.8, +48.9, +67.3 percentage points
respectively).
- AI systems are increasingly capable at video generation and programming under
time constraints, occasionally outperforming humans in restricted settings.
- In healthcare, theFDAapproved 223 AI-enabled medical devices in 2023 (versus
just 6 in 2015).
- Self-driving and robotic mobility solutions are scaling: e.g. Waymo giving
150,000 autonomous rides weekly; Baidu’s Apollo Go robotaxi deployed across
Chinese cities.
- In 2024, U.S. private AI investment reached $109.1 billion — far exceeding
China’s $9.3B and the U.K.’s $4.5B.
- Generative AI alone drew $33.9 billion globally, an increase of 18.7% over 2023.
- 78 % of organizations reported using AI by 2024, up from 55% in 2023.
- U.S. institutions produced 40 “notable” AI models in 2024, compared to 15 in
China and 3 in Europe.
- U.S. federal agencies proposed 59 AI-related regulations in 2024 — more than
twice the number in 2023.
- In the U.S., computing bachelor’s degrees have grown 22% over the past decade.
- Among U.S. K–12 CS teachers: 81% believe AI should be included in foundational
CS education, but less than half feel prepared to teach it.
- 90% of “notable” AI models in 2024 originated from industry (versus 60 % in
2023).
- Academia still leads in highly cited research.

5. - Scale continues to grow: training compute doubles every 5 months, datasets
every 8 months, power use annually.
- TwoNobel Prizesacknowledged deep learning foundations (physics) and
applications (protein folding in chemistry).
- TheTuring Awardalso honored advances in reinforcement learning.
- While AI models perform well on many tasks (e.g. Olympiad math problems),
they struggle with logic and precise reasoning benchmarks (e.g. PlanBench).
- This limitation is especially relevant in high-stakes domains where error
tolerance is low.
AI developments are not just occurring in research labs; they are also garnering
attention from the general public and publications devoted to education.
A variety of ideas and frameworks for ethical AI, as well as for associated ideas
like human-centered, egalitarian, and responsible AI, have been developed by
researchers. Participants in the listening session called for expanding on these
ideas and frameworks while acknowledging the need to go further. They pointed
out that, given the speed at which AI is being incorporated into mainstream
technologies, there is an urgent need for rules and regulations that ensure the
safe use of AI advancements in education. Together, policymakers and
stakeholders in education must begin defining the requirements, disclosures,
rules, and other frameworks that can help create a secure and happy future for all
parties involved, particularly kids and teachers, as policy creation takes time.
We've highlighted how adaptivity is impacted by AI advancements, but we've also
highlighted how adaptivity is constrained by the intrinsic qualities of the model.
We pointed out that the term "personalized" was employed differently in a
previous wave of edtech, and that it was frequently necessary to define what
personalization meant for a certain good or service. Therefore, our main
suggestion is to identify the advantages and disadvantages of AI models in
upcoming edtech products and concentrate on AI models that closely match
desired learning visions. Since artificial intelligence is now developing quickly, we
need distinguish between products with basic AI-like capabilities and those with
more complex AI models.

6. There is a noticeable push and effort being made to overcome these restrictions
when we look at what is occurring in research and development. We pointed out
that since there is no such thing as generic artificial intelligence, decision makers
should exercise caution when choosing AI models that could limit their capacity
for learning.
Furthermore, we must continue using systems thinking, which involves people in
the loop and takes into account the advantages and disadvantages of the
particular educational system, as AI models will always be more limited than real-
world experience. We maintain that the learning system as a whole is more
comprehensive than just its AI component.
Potential Benefits of AI in Education:
- Personalized Learning: AI can tailor educational content to each student's
individual pace, style, and needs, leading to deeper engagement and
understanding.
- Increased Efficiency: AI tools can automate tasks like grading and administrative
duties, freeing up educators' time to focus on teaching and student support.
- Enhanced Accessibility: AI can provide access tohigh-quality educational
resourcesand virtual tutoring, potentially bridging gaps for diverse learners.
- Improved Feedback: Students can receive real-time, detailed feedback on their
work, which helps them identify strengths and weaknesses and improves learning
outcomes.
- Data-Driven Insights: AI can provide educators with valuable data on student
performance, helping them to identify trends and areas for instructional
improvement.
Potential Downsides of AI in Education:
- Privacy and Security Risks: AI systems collect and process sensitive student data,
raising concerns about data privacy and the potential for misuse(source:
https://www.eschoolnews.com/digital-learning/2024/02/05/what-is-the-
impact-of-artificial-intelligence-on-students/).

7. - Algorithmic Bias: AI models can perpetuate and even amplify biases present in
the data they are trained on, leading to unfair or inequitable outcomes in
assessments.
- Over-Reliance on Technology: Students may become too dependent on AI tools,
which could hinder the development of essential non-cognitive skills and creative
problem-solving.
- Reduced Human Interaction: An overemphasis on technology might lead to less
face-to-face interaction, impacting students' social and emotional development.
- Implementation Costs and Skills: The initial cost of implementing AI systems can
be high, and teachers may lack the necessary skills or resources to use these tools
effectively.
Teachers have a famously difficult profession because they have to make
thousands of judgments every day. Teachers take part inclassroomoperations,
interactions with students outside of the classroom, collaboration with other
educators, and administrative duties. They are required to engage with families
and caregivers because they are also members of their communities.
We consider how much simpler some daily chores have gotten. We are able to
send and receive event alerts and notifications. Even with digital music, choosing
the music we want to listen to used to require a number of steps. However, these
days, we can simply say the name of a song we want to hear, and it will start
playing. Similar to how mapping a route used to involve a laborious study of
maps, cell phones now allow us to select from a variety of modes of
transportation in order to get to our destination. Why can't educators be given
the tools they need to implement a technology-rich lesson plan and the assistance
they need to recognize the evolving requirements of their students? Why is it so
difficult for them to arrange the learning paths of their students? Since classroom
dynamics are continually changing, why don't the resources available to
instructors help them quickly adjust to the needs and skills of their students?
The loop that decides which resources are available and what they do in the
classroom is the most comprehensive one in which teachers should participate.
Teachers are already involved in the design and selection of technologies
nowadays. Teachers can comment on practicality and usability. Teachers look at

8. efficacy data and report back to other school administrators on their results.
Teachers already exchange ideas about how to effectively use technology.
These worries will persist, but AI will also give rise to new ones. These worries go
beyond data security and privacy; they draw attention to the ways in which
technology may unjustly restrict or guide some kids' educational opportunities.
One important lesson to be learned from this is that instructors will require time
and assistance to stay up to date on both the more recent and well-known
challenges that are emerging, as well as to fully engage in risk-reduction design,
selection, and evaluation processes.
Using the teacher's knowledge of the needs and strengths of each student, AI
could assist educators in personalizing and tailoring resources for their students.
Customizing curriculum materials takes a lot of effort, and educators are already
looking at how AI chatbots may assist them in creating new materials for their
students. An elementary school teacher could receive strong support for altering
a storybook's illustrations to excite their kids, changing vocabulary that doesn't fit
local speech patterns, or even rewriting narratives to include additional
educational components. We pointed out that AI might be useful in determining
a learner's capabilities. When a student is in another teacher's physics class, for
instance, a math teacher might not be aware of how they are understanding
graphs and tables regarding motions, and they might not see that utilizing
comparable graphs about motion could aid in their lesson on linear functions. By
developing or modifying educational materials, AI may assist educators in
identifying and utilizing students' abilities. However, the four pillars we
previously described—human in the loop, equity, safety and efficacy, and
evaluation of AI models—must be used to address the wide equity concerns of
preventing algorithmic prejudice while enhancing community and cultural
responsiveness.
We now add another layer to our criteria for good AI models based on the needs
of teachers (as well as students and their families/caregivers):explainability.
Some AI models are able to identify patterns in the world and take the
appropriate action, but they are unable to provide an explanation for their actions
(e.g., how they came to the connection between the pattern and the action).
Teachers will need to understand how an AI model evaluated a student's work
and why the model suggested a specific tutorial, resource, or next step to the
student. This lack of explainability won't be enough for instruction.

9. Therefore, a teacher's capacity to evaluate an AI system's conclusion depends on
how explainable it is. Teachers can create appropriate levels of confidence and
distrust in AI with the aid of explainability AI, especially when it comes to
identifying areas where the AI model tends to make bad decisions. Explainability
is also essential for teachers to be able to spot instances in which an AI system
can be acting unfairly based on incorrect information.
The concept of explainability revolves around the requirement that educators be
able to examine the actions of an AI model. For instance, which pupils are
receiving what kinds of instructional recommendations? In a never-ending cycle,
which kids are receiving remedial assignments? Which are advancing?
Dashboards in existing products show some of this data, but with AI, educators
might want to learn more about which decisions are being made, for whom, and
what student-specific factors an AI model had access to (and perhaps which
factors had an impact on a given decision). Some of the adaptive classroom tools
available today, for instance, use limited recommendation models that only take
into account a student's performance on the last three math problems; they
ignore other factors that a teacher would be aware to take into account, like
whether a kid has anIndividualized Education Program (IEP)or other needs.
Information about how discriminatory bias may manifest in specific AI systems
and what developers have done to overcome it is necessary to support our plea
for equality issues to be taken into account when evaluating AI models. This can
only be accomplished by being transparent about how the tools employ datasets
to achieve results and what data they have on hand or that a teacher may use to
make decisions but that the system does not have access to (the example above
uses IEP status).
Additionally, teachers will need to be able to observe and judge automated
judgments, like which set of arithmetic problems a student should work on next,
for themselves. When they disagree with the reasoning behind an instructional
advice, they must have the ability to step in and override decisions.46 When
teachers exercise human judgment over an AI system's choice, they must be
protected from unfavorable consequences.
Formative assessments may be strengthened by AI models and AI-enabled
technologies. For instance, AI algorithms can be used to assess a question type
that asks students to draw a graph or construct a model, and then combine
comparable student models for the teacher to interpret. Teachers may be able to

10. respond more effectively to students' comprehension of a topic like "rate of
change" in a complicated, real-world scenario if they use enhanced formative
assessment. Additionally, AI may provide feedback to students on difficult skills
like speaking aforeign languageor learning American Sign Language, as well as in
other practice scenarios where no human is available to offer prompt input.
In general, teachers may find that an AI helper can lighten their workload by
evaluating easier parts of student responses, freeing up their specialized
judgment to concentrate on crucial elements of a lengthy essay or intricate
project. With accessibility, we might also be able to give comments more
effectively. Without requiring the student to view a screen or type at a keyboard,
an AI-enabled learning tool might, for instance, be able to speak with them about
how they responded to an essay prompt and give them questions that help them
to clarify their position.
As demonstrated by the examples presented earlier, artificial intelligence (AI) can
be integrated into the learning process to provide students feedback while they
are working on a problem rather than after they have arrived at an incorrect
solution. More integration of formative assessment can enhance learning, and
prompt feedback is essential.
Even though there are many similarities between AI and formative assessments,
our listening sessions also showed that participants wanted to address some of
the formative assessment's current drawbacks, such as how time-consuming and
occasionally burdensome tests, quizzes, and other assessments are, as well as
how little teachers and students value the feedback loop.
A few AI-powered tools and systems aim to resolve this possible contradiction.
One AI-enabled reading tutor, for instance, listens to students read aloud and
offers immediate feedback to help them read better. Students indicated that
reading aloud was enjoyable, and the method worked. In order to allow students
to demonstrate their mastery ofNewtonian physicsas they progress through
increasingly challenging game levels, researchers have also incorporated
formative assessments into games. If students can more readily ask for and
receive assistance when they are feeling confused or frustrated, it can be a
positive thing. To demonstrate their learning, students must feel secure, certain,
and trusting of the feedback produced by these AI-enabled tools and systems. It
is best to concentrate on learning progress and gains (without unfavorable
outcomes or a high-stakes setting).

11. AI-enhanced formative exams could potentially free up teachers' time (such as
time spent grading) so they can devote more time to student assistance.
Teachers may also gain from enhanced assessments if they offer comprehensive
insights into students' needs or strengths that aren't always apparent and if they
encourage instructional modification or development by offering a limited
number of recommendations based on research to assist students grasp the
material. If these tests can give feedback when the teacher is not there, such
when students are doing their homework or rehearsing a topic during study hall,
they might also be useful outside of the classroom. As we covered in the
Teaching section, putting instructors at the heart of system design is crucial to
implementing AI-based formative assessment.
Adoption decisions are also heavily influenced by educators, students, and their
families/caregivers. When educators challenge or overrule an AI model based on
their professional judgment, parents and leaders must stand by them.
Technology developers must be open about the models they employ, and
legislators may need to establish transparency standards so that the market may
operate based on knowledge about AI models rather than just assertions of their
advantages.
Many key ideas, such as how to arrange learning activities and provide students
with feedback were incorporated. However, the fundamental idea was frequently
deficit-based. The algorithm selected pre-existing learning materials that would
address the student's weakness by concentrating on what was wrong with them.
We need to use AI's capacity to identify and capitalize on learner strengths going
future. We also know that learning is strongly social and that people are
inherently social, despite the individualistic techniques of the last years(source:
https://ijisae.org/index.php/IJISAE/article/view/5928/4680).In the future, we
must develop AI capabilities that align with social and collaborative learning
concepts and value students' entire human skill set, not just their cognitive ability.
In the future, we must also work to develop AI systems that are both culturally
sensitive and culturally sustaining, taking advantage of the expanding body of
documented methods for this purpose. Additionally, the majority of early AI
systems offered limited assistance for English language learners and pupils with
disabilities. In the future, we need to make sure that learning materials powered
by AI are purposefully inclusive of these pupils. Edtech that enhances each
student's capacity for decision-making and self-control in progressively
complicated situations has not yet been developed by the field. We must create

12. educational technology that enhances students' capacity for creative learning as
well as their capacity for discussion, writing, leadership, and presentation.
Additionally, we urge educators to reject AI applications that rely only on machine
learning from data, without incorporating knowledge from experience and
learning theory. It takes more than just processing "big data" to create equitable
and successful educational systems, and while we want to use data to gain
insights, human interpretation of data is also crucial. We oppose technological
determinism, which holds that data patterns alone dictate our course of action.
AI applications in education must be based on well-established, contemporary
learning theories, the knowledge of educational professionals, and the knowledge
of the educational assessment community on bias detection and equity
enhancement.
So, is AI good or bad for education? The answer is not simple.
AI offers enormous potential to personalize learning, improve access, and support
teachers(source:https://www.nea.org/resource-library/artificial-intelligence-
education).It can reduce administrative burdens and provide valuable insights
into student performance. At the same time, it raises pressing concerns about
privacy, ethics, inequality, and the erosion of critical thinking. Ultimately, AI in
education is neither inherently good nor bad—it is a tool. Like any tool, its impact
depends on how we use it. If integrated thoughtfully, with safeguards for ethics
and equity, AI could transform education for the better. If adopted recklessly, it
risks undermining the very goals of learning. The key lies in balance: embracing
innovation while preserving the human heart of education. Teachers, students,
and policymakers must work together to shape a future where AI empowers
rather than replaces, complements rather than dominates.
Teachers have already risen to the task of developing broad standards, coming up
with targeted applications for the AI-enabled tools and systems that are currently
accessible, and identifying issues. However, it is impossible to predict how
educators will affect AI-enabled goods in the future; instead, stakeholders require
policies that support this. Would it be possible to establish a national corps of top
educators from each state and area to serve as leaders? Could we make a
commitment to creating the supports for professional development that are
required? Can we figure out how to pay teachers so they can play a key role in
shaping education's future? Teachers should be allowed to actively participate in
the development of AI-enabled educational systems thanks to the new policies.

13. Jeff Palmeris a teacher, success coach, trainer, Certified Master of Web
Copywriting and founder ofhttps://EbookACE.com. Jeff is a prolific writer, Senior
Research Associate and Infopreneur having written many eBooks, articles and
special reports.
Source: https://ebookace.com/is-artificial-intelligence-good-or-bad-for-
education/