On 29th November 2024, the AI, Ethics, and Society Group at Queen Mary University of London hosted Impact of AI in Education: Challenges and Considerations at the Digital Environment Research Institute (DERI). This event brought together educators, researchers, and professionals to explore artificial intelligence's profound and evolving role in education. The discussions addressed both current challenges and emerging opportunities, with a focus on ethical considerations, practical implementations, and forward-looking recommendations.
The event opened with a panel discussion moderated by Dr. George Fazekas, Senior Lecturer at the School of Electronic Engineering and Computer Science (EECS). The panel featured directors of education in different schools, including Dr. Karen Shoop, Deputy Director of Education in the School of Engineering and Materials Science; Dr. Sally Faulkner, Director of Teaching in the School of Biological and Behavioural Sciences; Dr. Eleni Hagi-Pavli, Director of Graduate Studies at the Institute of Dentistry; and Shoshi Ish-Horowicz, Head of Innovation and Learning at Queen Mary Academy. Together, the panellists shared their insights on how AI tools are being integrated into teaching practices across disciplines, highlighting both the benefits and the challenges faced by educators. Discussions touched on issues such as AI-assisted learning, academic integrity, student engagement, and the need for ethical frameworks to guide AI adoption in educational settings.Following the panel, participants engaged in breakout sessions, working in small groups to delve deeper into key themes. The sessions focused on identifying best practices for integrating AI into teaching, proposing new directions for enhancing student engagement and reimagining assessment methods in an AI-enabled landscape. The discussions were interactive and productive, utilizing whiteboards, sticky notes, and tools like XMind to capture ideas and recommendations. Participants highlighted the importance of sharing practical experiences while considering AI solutions' scalability across diverse disciplines. Ethical considerations like data privacy and biases in AI-driven assessments were central to these conversations.
During the workshop, we asked three main questions:
Question 1: What best practices have you observed or implemented in integrating AI tools within educational settings, and how have these impacted teaching and learning outcomes?
Question 2: What new approaches or innovative strategies do you recommend for enhancing the use of AI in educational environments to improve student engagement and learning experiences?
Question 3: How should current assessment methods evolve to effectively incorporate the capabilities and challenges posed by AI in education, ensuring both academic integrity and comprehensive evaluation of student learning?
Thematic Analysis emerging from these three questions
This thematic analysis examines the data collected from workshops on the integration of AI tools in education, focusing on best practices, challenges, and ethical considerations. The responses provide insights into how AI is enhancing learning processes while highlighting critical concerns.
Several best practices emerged regarding AI's integration into education. AI tools have been effectively used to critique journal articles, generate content prompts, and encourage collaborative discussions. By integrating AI into these tasks, educators enable students to develop critical analysis skills and engage with content at a deeper level. Additionally, AI tools such as coding assistants allow students to shift their focus from repetitive tasks to higher-order thinking, including problem-solving strategies and algorithm design. In creative disciplines, AI-generated prompts and content, such as artwork, encourage students to critique and analyze outputs, fostering deeper understanding and creativity. AI also enhances learning customization through tailored questions, exercises, and open-ended prompts that align with real-world applications. This personalized approach facilitates authentic assessments and encourages students to explore concepts analytically. Furthermore, AI-generated visual or textual content has been used as a starting point for classroom discussions and critical follow-up exercises, which encourage analytical thinking and active participation.
Despite these benefits, several challenges were identified. One key concern is the risk of overreliance on AI, particularly in introductory subjects like programming. AI tools can bypass foundational learning processes, hindering students from acquiring essential skills such as debugging or understanding basic constructs. Another challenge lies in the accuracy and originality of AI-generated outputs. AI tools sometimes produce incorrect references or repetitive content based on patterns in training data, which limits reliability and novelty. Educators and students must critically review AI outputs to mitigate this issue. Equity and access were also highlighted as significant challenges. Unequal access to AI tools, particularly premium versions, risks widening educational disparities among students from diverse economic backgrounds. Additionally, biases within AI systems, stemming from skewed training datasets, can reinforce societal inequalities such as gender or racial bias, raising the need for critical awareness and intervention.
Ethical considerations are central to the responsible integration of AI in education. Academic integrity and accountability must be maintained through the transparent acknowledgment of AI use by both students and educators. Misuse, such as plagiarism or shortcuts, raises concerns that require clear institutional guidelines to ensure responsible engagement. Data privacy is another critical issue, as inputting student or institutional data into AI tools risks exposing sensitive information. Protecting data and understanding the implications of its use are essential for mitigating these risks. Furthermore, addressing biases in AI outputs and ensuring transparency regarding the origins of AI-generated content are vital to fostering critical engagement among students. Institutions must also address the digital divide to ensure equitable access to AI tools, preventing existing learning gaps from widening.
In terms of assessment, evolving methods to incorporate AI emerged as a key recommendation. Reflective assessments, where students generate content with AI and then critically analyze or revise it, promote active engagement and critical thinking. Oral examinations were suggested as a complement to written tasks, enabling educators to evaluate students' understanding, reasoning, and analytical skills independently of AI tools. Process-oriented assessments that emphasize the articulation of thought processes and the demonstration of understanding ensure that students engage deeply with content rather than relying passively on AI outputs. Equitable implementation of AI tools in assessments, supported by clear policies, is necessary to maintain fairness and accessibility.
The workshop responses highlight both the opportunities and challenges of integrating AI into education. While AI tools can enhance critical thinking, creativity, and authentic learning experiences, their implementation must be carefully managed to address overreliance, inequities, and ethical concerns. Transparency, data privacy, and bias mitigation are essential considerations in ensuring the responsible use of AI. By emphasizing reflective assessments, balancing foundational learning, and promoting equitable access, educators can harness AI's potential to create meaningful and inclusive learning outcomes.
Author: Elona Shatri