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.