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Will AI revolutionize education for the better?

Another thrilling debate with both the propositions Sheila and Teagan and oppositions Deagan and Jessalyn putting forth strong arguments. The debate reflected both enthusiasm and trepidation around the incorporation of artificial intelligence (AI) in education. As AI becomes more ingrained in classrooms, the debate presents fundamental questions regarding the role of teachers, the integrity of learning and the trajectory of future curricula.  The opposing sides presented passionate and divergent positions, each stressing the transformative possibilities of AI along with its potential risks. 

Sheila and Teagan pointed out that AI was not something to be feared but instead should be seen as a driver for educational innovation. They argued for a change in assessment methods, suggesting that educators should adopt approaches in which AI usage would be reduced or made irrelevant. Their argument is supported by research in the peer-reviewed article “Lessons learned for AI education with elementary students and teachers“, which makes the case for AI literacy among young students. The article presents AI as the next technological advancement and the necessity for citizens to be knowledgeable about AI, both conceptually and ethically. Notably, it points out educator readiness as a critical factor, since teachers’ confidence in AI use significantly influences its adoption in teaching. 

Although the call for new testing practices that work around AI might sound like a progressive solution, it threatens to overlook the larger educational opportunity AI affords. Avoidance can create short-term control, but constructive learning of AI in the long-term demands’ integration, thus it’s better to teach AI literacy.

On the contrary Deagan and Jessalyn cautioned that AI has the potential to cause more harm than benefit in education. They contend that overdependence on AI hinders critical thinking, encourages intellectual shortcuts, and compromises the integrity of student work. Among their strongest arguments was that “fast is not always efficient”, a rebuke of the tendency to value AI’s speed over the longer cognitive processes required for substantive learning. This concern is mirrored in the argument posed by Thompson (2025), who contends that AI has created “intellectual roadblocks,” degraded the teacher-student dynamic, and presented a host of ethical and educational issues. Thompson emphasizes that AI is frequently not being utilized constructively and that its presence in the classroom threatens to stifle development rather than promote it. 

While the proposition focused on flexibility and the educational potential of AI, the opposition cautioned and called for a recommitment to traditional cognitive growth. There is one point both sides agree on, though: AI in education is unavoidable, it is the “how” that is up for debate. Certainly, AI integration in education is profound and increasingly difficult to disregard. It can provide personalized learning pathways, automate routine administrative tasks, and provide real-time feedback to students and teachers. Such efficiencies enable educators to devote more time to student engagement and creativity. For students with learning differences, AI can provide tailored assistance, such as text-to-speech tools or adaptive testing, making education more accessible. 

Conversely, the downsides are no less urgent. Among the most troubling, according to both oppositions and Thompson (2025), the degradation of critical thinking and deep learning. When students use AI to do assignments or provide answers, they can circumvent the mental effort needed to actually learn and implement knowledge. There are also ethical and privacy issues, such as algorithmic bias, data exploitation and lack of transparency regarding how AI systems function. In addition, the digital divide may further expand, with under-resourced schools and students left behind in the AI revolution. While AI planning, clear policy, and a robust ethical framework are necessary to ensure it improves rather than detracts from the learning process. 

Finally, AI’s impact on education is not inherently good or bad, but its effects will rest in purposeful implementation. Teachers will not only need to teach with AI but also about AI, raising ethical, knowledgeable users of technology. As researchers, policy makers, and classroom educators of the future, we need to see that AI is not a substitute for human teaching but a tool that. If used judiciously, it may enhance and augment it. 

Finally, the debate made it clear that AI’s impact on education is not inherently good or bad, but that its effect will rest in purposeful implementation. Teachers will not only need to teach “with” AI but also “about” AI—raising ethical, knowledgeable users of the technology. As researchers, policymakers, and classroom educators of the future, graduate students need to see that AI is not a substitute for human teaching but a tool that, if used judiciously, may enhance and augment it. The revolution AI threatens is genuine, but its path rests in how boldly and responsibly we lead its integration.

 

In EC&I 830 class, we were tasked with critically exploring the promises and pitfalls of educational technology through facilitated, research-based debates. The debates spanned a variety of timely topics, including AI in the classroom, the effects of social media on childhood, cellphone bans, and whether we should continue to teach traditional skills like handwriting. Each topic compelled me to think more critically about the nuanced and often polarized discussions surrounding technology in education. I realized that while technology could undoubtedly augment learning and access, its implementation needed to be considered carefully with equity, mental health, and instructional integrity in mind. 

As a teacher, this course reinforced my conviction in the benefits of technology integration, while also transforming my vision. I now advocate for the implementation of policies that are grounded in solid foundations and involve the participation of all stakeholders, including teachers, students, parents, and administrators. Technology is no longer a choice in education; it is woven into the fabric of our students’ lives and learning spaces. As such, we must make it a priority to instruct digital literacy as a core skill, not just to operate tools but to critically interact with the digital world. 

I am dedicated to promoting responsible, purposeful, and inclusive tech use in my classroom while being attentive to its social and emotional effects. 

I appreciate the support of Dr. Hilderbrandt and the valuable input from all my insightful classmates who have been a part of my educational journey.

 

In an age where artificial intelligence and automation adeptly take on numerous traditional tasks, a significant question arises: Should schools continue to prioritize foundational instructional skills, such as cursive writing, multiplication tables, and spelling? The conversation is often framed as a binary choice: either we uphold outdated practices or fully embrace a digital-first approach. However, this dichotomy oversimplifies the issue. Foundational skills are not merely relics of the past; they are essential building blocks for cognitive development and digital fluency. I contend that foundational skills in math and literacy form the necessary scaffolding for advanced competencies, such as coding, digital problem-solving, and critical media literacy. My observation is that students who excel in math and reading tend to perform exceptionally well in coding and navigating technology.

During the debate, both teams highlighted key tensions in contemporary pedagogy. Brianne and Rose argued that teaching cursive writing is increasingly obsolete in an era where digital tools dominate communication. They advocated reallocating time to more “relevant” skills like keyboarding and coding, aligning with broader curricular reforms aimed at modernizing education.  

However, this perspective narrowly frames education as a utilitarian process, ignoring the cognitive and neurological benefits of foundational skills. Vanessa and Jenna presented a holistic view grounded in research from the National Education Association (NEA), arguing that cursive writing promotes memory retention, fine motor coordination, and brain activation that typing does not engage—all factors that I can attest to. According to Betty Blog (2024), handwriting activates more brain regions and connections when compared to typewriting.  Instruction should therefore be designed to support the whole learner, not just job-ready competencies.

Research supports the argument that foundational literacy and numeracy significantly contribute to students’ overall academic cognitive development. According to an article in the Calgary Sun (2014), Alberta’s educational outcomes suffered when curricula neglected basic math skills in Favour of conceptual understanding. Parents and educators reported that students were unable to perform simple calculations without technological assistance, leading to widespread concerns over “math illiteracy” (Leong, 2014). This example illustrates the dangers of prematurely abandoning foundational skills in the name of innovation. 

Similarly, a report from LatinHire (2024) stresses the importance of math fluency as a prerequisite for more complex cognitive tasks, including programming and data analysis. It identifies five compelling reasons why students must develop math foundations, including problem solving, logical thinking, and the ability to connect abstract concepts to real-world applications. These skills not only support traditional academic success but also underpin competencies required for STEM and digital fields (“5 reasons Why Kids Need Math,”2024).

Bridging the Gap

Students thrive when foundational and digital skills are taught in tandem, rather than in isolation. My stance is shaped by both educational research and classroom experience: when students master foundational reading and math skills, they are better equipped to excel in digital literacy and computer science. Coding, for example, requires logical sequencing, pattern recognition, and comprehension, abilities rooted in traditional curricula. Moreover, spelling and vocabulary enhance algorithmic thinking and language syntax comprehension, directly benefiting students learning programming languages. 

The belief that school is “boring and irrelevant,” as reported in a Telegraph article surveying teenagers (2009), is often a result of disconnected and outdated instruction, not the content itself. The challenge, then, is not to discard cursive or multiplication tables, but to reimagine their delivery in a way that links them to students lived realities and future aspirations.

For example, in one documented case at an elementary school in British Columbia, teachers using MindUP reported a significant increase in student engagement and motivation, as students better understood how their classroom learning connected to personal goals and community involvement (Schonert-Reichl et al., 2015). MindUP is a program developed by The Goldie Hawn Foundation. This program integrates neuroscience, social-emotional learning, and mindful awareness into core subjects like math and literacy. Blended learning models and project-based instruction can anchor traditional skills within meaningful, technology-enhanced contexts.

The Move Toward Integration

The current educational landscape demands a nuanced approach, one that values both tradition and innovation. Foundational skills such as cursive writing, spelling, and multiplication tables serve cognitive, neurological, and academic purposes that extend far beyond their surface utility. When students develop these skills, they are better prepared to transition to complex, technology-dependent competencies. Rather than choosing between teaching cursive or coding, schools should embrace an integrated literacy framework that balances analog and digital literacies under clear, intentional policies. 

Educators, parents, and policymakers must recognize that foundational learning is not a barrier to progress; it is the launchpad. Ensuring that all students have access to both sets of skills will foster more equitable, empowered, and future-ready learners.

 

Will AI revolutionize education for the better?

Another thrilling debate with both the propositions Sheila and Teagan and oppositions Deagan and Jessalyn putting forth strong arguments. The debate reflected both enthusiasm and trepidation around the incorporation of artificial intelligence (AI) in education. As AI becomes more ingrained in classrooms, the debate presents fundamental questions regarding the role of teachers, the integrity of learning and the trajectory of future curricula.  The opposing sides presented passionate and divergent positions, each stressing the transformative possibilities of AI along with its potential risks. 

Sheila and Teagan pointed out that AI was not something to be feared but instead should be seen as a driver for educational innovation. They argued for a change in assessment methods, suggesting that educators should adopt approaches in which AI usage would be reduced or made irrelevant. Their argument is supported by research in the peer-reviewed article “Lessons learned for AI education with elementary students and teachers“, which makes the case for AI literacy among young students. The article presents AI as the next technological advancement and the necessity for citizens to be knowledgeable about AI, both conceptually and ethically. Notably, it points out educator readiness as a critical factor, since teachers’ confidence in AI use significantly influences its adoption in teaching. 

Although the call for new testing practices that work around AI might sound like a progressive solution, it threatens to overlook the larger educational opportunity AI affords. Avoidance can create short-term control, but constructive learning of AI in the long-term demands’ integration, thus it’s better to teach AI literacy.

On the contrary Deagan and Jessalyn cautioned that AI has the potential to cause more harm than benefit in education. They contend that overdependence on AI hinders critical thinking, encourages intellectual shortcuts, and compromises the integrity of student work. Among their strongest arguments was that “fast is not always efficient”, a rebuke of the tendency to value AI’s speed over the longer cognitive processes required for substantive learning. This concern is mirrored in the argument posed by Thompson (2025), who contends that AI has created “intellectual roadblocks,” degraded the teacher-student dynamic, and presented a host of ethical and educational issues. Thompson emphasizes that AI is frequently not being utilized constructively and that its presence in the classroom threatens to stifle development rather than promote it. 

While the proposition focused on flexibility and the educational potential of AI, the opposition cautioned and called for a recommitment to traditional cognitive growth. There is one point both sides agree on, though: AI in education is unavoidable, it is the “how” that is up for debate. Certainly, AI integration in education is profound and increasingly difficult to disregard. It can provide personalized learning pathways, automate routine administrative tasks, and provide real-time feedback to students and teachers. Such efficiencies enable educators to devote more time to student engagement and creativity. For students with learning differences, AI can provide tailored assistance, such as text-to-speech tools or adaptive testing, making education more accessible. 

Conversely, the downsides are no less urgent. Among the most troubling, according to both oppositions and Thompson (2025), the degradation of critical thinking and deep learning. When students use AI to do assignments or provide answers, they can circumvent the mental effort needed to actually learn and implement knowledge. There are also ethical and privacy issues, such as algorithmic bias, data exploitation and lack of transparency regarding how AI systems function. In addition, the digital divide may further expand, with under-resourced schools and students left behind in the AI revolution. While AI planning, clear policy, and a robust ethical framework are necessary to ensure it improves rather than detracts from the learning process.  

Finally, the debate made it clear that AI’s impact on education is not inherently good or bad, but that its effect will rest in purposeful implementation. Teachers will not only need to teach “with” AI but also “about” AI—raising ethical, knowledgeable users of the technology. As researchers, policymakers, and classroom educators of the future, graduate students need to see that AI is not a substitute for human teaching but a tool that, if used judiciously, may enhance and augment it. The revolution AI threatens is genuine, but its path rests in how boldly and responsibly we lead its integration.