Andreina Del Carmen Ovalles-Rodriguez
Prof. Fatimah Broxton-Robinson
ENG 101-301-251
January 17th, 2025
AI Literacy Integration in Education: AI’s Erosion of Students’ Interpersonal
and Non-academic Skills Will Jeopardize Their Employability in a
Job Market That Prioritizes Holistic Competencies
Alongside Technical Expertise.
In the rapidly evolving landscape of technology, artificial intelligence (AI) is becoming a
cornerstone of modern education. Its integration has enabled personalized learning, improved
administrative efficiency, and reshaped traditional pedagogical methods. However, as educators
and policymakers emphasize AI literacy, concerns arise about its unintended consequences on
students' interpersonal and non-academic skills. AI also poses a risk regarding ethics and data
which the full extent of is unknown, and which is largely unchecked. While AI literacy is
indispensable, over-reliance on AI in education risks eroding students' interpersonal skills and
holistic competencies, jeopardizing their future employability.
One of the most significant drawbacks of AI integration in education is the potential
erosion of students' interpersonal skills. Al-Zahrani, Chan, and Lee all highlight that overreliance on AI tools reduces opportunities for human interaction, a critical component of
interpersonal skill development. Chan emphasizes that face-to-face collaboration and
communication are irreplaceable in fostering empathy, teamwork, and conflict resolution (Chan,
9). When students primarily engage with AI-driven platforms, they miss out on these vital
experiences, leading to a generation less adept at navigating social and professional
environments (Al-Zahrani). Moreover, Chan underscores the importance of balancing
technological advancements with interpersonal and ethical skills. Her Holistic Competency
Development Framework (HCDF) advocates for integrating activities that cultivate interpersonal
abilities into AI-driven curricula. Chan argues that while AI can streamline learning processes, it
must not replace human-centric interactions essential for developing communication and
emotional intelligence. She further discusses how “students needed to improve a range of their
non-academic skills including teamwork, motivation, values, attitudes, integrity, creativity and
common sense – a broad skillset that I consider “life jewels”” (Chan, 2). Without deliberate
efforts to preserve these skills, students risk becoming proficient in AI literacy but deficient in
the competencies employers increasingly demand (Chan, 4)).
In addition to interpersonal skills, AI integration poses challenges to the development of
non-academic skills such as creativity, critical thinking, and problem-solving. Lee and Kwon
note that while AI applications enhance personalized learning and student engagement, they can
inadvertently lead to dependency on automated solutions. This dependency stifles students'
ability to approach problems independently and creatively, as they rely on AI for answers and
solutions (Lee and Kwon, 8). Al-Zahrani further highlights that algorithmic bias and data-driven
decision-making can narrow students' perspectives, limiting their ability to think critically. The
velocity of AI innovation often outpaces regulatory scrutiny, raising concerns about ethical and
cognitive implications. Al-Zahrani’s study identifies critical areas of concern such as privacy
breaches, algorithmic bias, and transparency, which not only hinder skill development but also
exacerbate inequities. Chan introduces the concept of AI-giarism, a new form of academic
dishonesty where students use AI-generated content without acknowledgment. To address this,
educators must design curricula that encourage students to question, analyze, and synthesize
information, even in the presence of AI tools (Al-Zahrani; Chan).
A critical drawback of AI integration in education is the ethical and practical concerns
surrounding data usage. Al-Zahrani emphasizes that AI systems rely on vast amounts of student
data, raising issues about privacy, consent, and potential misuse. Without stringent regulations,
this data could be exploited for purposes beyond education, such as targeted advertising or
discriminatory profiling. Furthermore, algorithmic bias embedded in AI systems can perpetuate
inequities, disadvantaging certain student groups and compromising the fairness of educational
outcomes. Chan underscores the importance of transparency and accountability in AI design,
advocating for systems that prioritize equity and ethical considerations. She warns that
unchecked data usage undermines trust in AI and risks normalizing practices that devalue student
autonomy and diversity. Lee and Kwon further identify the lack of comprehensive ethical
guidelines as a barrier to effective AI implementation in education. Addressing these concerns
requires robust data governance frameworks and educational curricula that teach students to
critically assess AI's societal implications, ensuring that the technology serves as a tool for
empowerment rather than exploitation (Al-Zahrani; Chan; Lee and Kwon).
To conclude, while AI literacy is a critical component of modern education, its
unbalanced integration risks eroding students' interpersonal and non-academic skills. These skills
are essential for employability in a job market that values holistic competencies alongside
technical expertise (Chan, 2). To mitigate these risks, educators and policymakers must adopt a
balanced approach, incorporating strategies that prioritize both AI literacy and interpersonal skill
development. As Chan advocates, a synergistic model can prepare students for the demands of a
rapidly evolving technological world while preserving their ability to connect, innovate, and
adapt. Lee and Kwon’s research further support the integration of hands-on and project-based
learning to inspire creativity and critical thinking, ensuring that students are prepared to navigate
both technical and societal challenges. The future of education lies not in choosing between AI
literacy and holistic competencies but in harmonizing them for the benefit of students and
society.
Works Cited
AL-ZAHRANI, A. M. Unveiling the shadows: Beyond the hype of AI in education. Heliyon, [s.
l.], v. 10, n. 9, 2024. DOI 10.1016/j.heliyon.2024.e30696. Disponível em:
https://research.ebsco.com/linkprocessor/plink?id=211993bb-a764-3a30-b637f0e7e5abfc5d. Acesso em: 8 jan. 2025.
This study explores the potential risks of integrating AI in education,
focusing on issues like data privacy, human connection, and algorithmic bias. Through
a literature review and survey of 260 participants, it highlights how these concerns are
interlinked and affect educational outcomes. The authors argue for a balanced
approach to AI adoption, emphasizing the need for ethical guidelines and strategies to
preserve interpersonal skills and equity in access.
CECILIA KA YUK CHAN. Holistic competencies and AI in education: A synergistic pathway.
Australasian Journal of Educational Technology, [s. l.], v. 40, n. 5, p. 1–12, 2024. DOI
10.14742/ajet.10191. Disponível em:
https://research.ebsco.com/linkprocessor/plink?id=21618bf3-1720-39f2-95b914fd281037f7. Acesso em: 8 jan. 2025.
Chan discusses the integration of AI and holistic competencies in education,
emphasizing the need to balance technological advancements with interpersonal and
ethical skills. She introduces the AI literacy model and addresses challenges such as
AI-giarism and AI guilt. She also highlights her Holistic Competency Development
Framework (HCDF) and initiatives like course accreditation and an AI-driven career
support platform. The editorial advocates for a student-centered, synergistic approach
to prepare learners for the demands of a rapidly evolving technological world. Chan
emphasizes the consequences of AI on students’ non-academic skills such as
interpersonal skills which are essential for employability, ethicality, and their future
contributions to society.
LEE, S. J.; KWON, K. A systematic review of AI education in K-12 classrooms from 2018 to
2023: Topics, strategies, and learning outcomes. Computers and Education: Artificial
Intelligence, [s. l.], v. 6, 2024. DOI 10.1016/j.caeai.2024.100211. Disponível em:
https://research.ebsco.com/linkprocessor/plink?id=139bfc4a-6fd8-3090-a6555d14e1e9a70e. Acesso em: 8 jan. 2025.
This systematic review examines the use of AI in K-12 education, focusing
on various applications including personalized learning, administrative tasks, and
pedagogical strategies. The study highlights how AI can enhance learning outcomes,
student engagement, and teacher efficiency, while also discussing challenges such as
data privacy, equity, and the need for teacher training.