Blossoming Innovations and Thorny Challenges

by Florence Williams, Patricia Farless, Jo Smith, Karen Haslett, Katia Ferdowsi

AI systems used in education require strong student data protection measures and deliberate efforts to reduce algorithmic bias. The most effective educational AI tools emerge from partnerships between technology developers and teaching experts, helping to ensure these systems work fairly and consistently for all students. 

Abstract

Harnessing AI for a smarter future in higher education is crucial as instructional designers and faculty work towards scaling and implementing AI ethically. This journey includes innovative explorations, ethical considerations, and practical applications. The integration of AI in education has sparked significant interest and debate among educators and researchers. AI technologies such as Claude, Copilot, Gemini, and ChatGPT are being used for content creation, assessment, and virtual help, promising increased efficiency and personalized learning experiences. However, these advancements come with challenges that need careful consideration (Kadry, 2024).   

Key Concepts of AI in Education

AI in education encompasses a range of applications from personalized support, virtual assistants, content generation, learning analytics, accessibility tools, to automated feedback. These technologies can streamline administrative tasks, provide personalized feedback, and support student inquiries. The goal is to enhance the educational experience while supporting ethical standards and data privacy (Walter, 2024). 

Case Studies in AI Implementation for Higher Education

Case Study 1: History Education – AI-Supported Historical Thinking and Ethics

Problem: The thorny challenge is providing effective guidance and feedback to students in fully asynchronous history courses while upholding disciplinary standards that promote critical thinking through historical research (analyzing primary sources and historiographical debates) and scholarly writing (Ryzheva et al., 2024). 

Solution: The rose buds are two-fold: 

A notable benefit of the program has been its strategic integration of artificial intelligence (AI) tools to enhance students’ engagement with historical inquiry and improve the quality and timeliness of feedback throughout the research process. As Hargrave et al. (2025) observe, this iterative model of learning allows students to refine their historical thinking skills across multiple stages of their work. Beginning with topic selection, AI supports students in formulating viable research questions and identifying appropriate primary sources. During the research phase, tools such as Elicit and Consensus enable learners to locate and critically evaluate historiographical debates, while large language models like ChatGPT offer formative guidance on thesis development. As students proceed to drafting, AI-powered writing assistants provide grammar suggestions and citation verification, giving students near-instant feedback that complements instructor input. These AI-supported checkpoints for topic formulation, investigation, adjustment, composition, reflection, and revision serve as sustained opportunities for academic growth across a variety of historical subjects (Bowen & Watson, 2024). 

Emerging as a promising bud, the program has also deepened students’ civic literacy by embedding AI within ethical deliberation practices. Drawing on the principles of representative ethics, AI was used not simply as a technical aid but as a catalyst for meaningful dialogue around historical interpretation and source credibility (Singh et al., 2025). Within a “Lockean classroom” environment, students co-constructed shared norms around the responsible use of AI in historical research. These included agreed-upon protocols for fact-checking AI-generated content against primary materials and upholding academic integrity in the use of generative tools (Walter, 2024). This democratic approach to classroom ethics signals the potential of AI not only to support knowledge production, but also to cultivate critical, participatory dispositions among learners. 

Nevertheless, a significant thorn remains in the ongoing negotiation of students’ reliance on AI and their ability to develop independent scholarly judgment. While the affordances of AI are considerable, the program continues to grapple with the risk that students may default to algorithmic convenience rather than engage in deeper analytical reasoning. Sustained instructor scaffolding and community-driven norms are essential to ensuring that AI enhances rather than displaces historical thinking. 

Impact: The resulting impact is a learning environment where instructors can shift their emphasis from mechanical aspects of writing to higher-order concerns such as critical analysis and historiographical reasoning. Simultaneously, students benefit from more consistent, targeted feedback than traditional methods typically afford. This enhanced support structure fosters deeper engagement with historical methods and contributes to improved research quality (Shah, 2023). 

Case Study 2: Medical Education – AI for Clinical Reasoning and Simulation

Problem: The thorny challenge involves providing authentic work practice opportunities for medical students that develop critical clinical reasoning skills, particularly when clinical placements are limited and standardized patients are expensive to coordinate (Treatment.com AI Inc., 2024). 

Solution: The rose buds emerged through implementation of AI-powered simulation platforms that offer students diverse clinical scenarios and practice examples, facilitating applied learning experiences that complement traditional instruction (Kadry, 2024). The program integrated AI-driven case study generators that create realistic patient presentations based on specific learning objectives, allowing students to practice diagnostic reasoning with immediate feedback. Students engage with AI chatbots programmed to simulate patient interactions, helping them develop communication skills and bedside manner isn a low-stakes environment. Additionally, AI tools analyze student responses to clinical scenarios, identifying knowledge gaps and suggesting targeted study resources. The system tracks student progress across multiple practice sessions, providing instructors with detailed analytics on individual and class-wide learning patterns. 

Impact: The roses blossomed as students demonstrated improved clinical reasoning abilities based on AI-enhanced resources, with assessment scores showing significant improvement in diagnostic accuracy and clinical decision-making (Hwang et al., 2020). Students reported increased confidence in patient interactions and greater motivation to engage with complex cases. The AI system’s ability to provide immediate, personalized feedback supported continuous learning progression, while instructors could focus class time on advanced concepts rather than remedial skill building. The program also reduced costs associated with standardized patient encounters while providing more diverse and frequent practice opportunities than traditional methods allowed. 

Case Study 3: Integrative General Studies – AI Literacy and Career Readiness 

Problem: The thorny challenge centers on improving accessibility and learning experiences for students transitioning to employment while ensuring they develop AI literacy essential for modern workplaces (Cope & Kalantzis, 2019). 

Solution: The rose buds blossomed through developing structured AI practices that assist students in crafting professional materials and understanding AI’s role in hiring processes (Hargrave et al., 2025). This included transforming the Professional Portfolio assignment to incorporate AI tools for resume and cover letter development, along with implementing a mini-AI module featuring an AI Playground assignment that provides hands-on experience. 

Impact: As students enhanced their ability to navigate AI-driven work environments and create competitive application materials, we saw roses in closing the technology gap between varied student populations and providing practical experience with ethical AI utilization (Ryzheva et al., 2024). 

UCF’s Integrative General Studies program has overcome several challenges to help seniors become more AI-savvy before graduation. One challenge is that AI is already transforming the hiring process, with many employers using AI to review applications. To help students stay competitive, the program transformed its Professional Portfolio assignment by allowing students to use AI tools when crafting resumes and cover letters, teaching them how to leverage AI effectively in real-world job applications (Shah, 2023). Another challenge is that not all students are equally tech-savvy. To bridge this gap, the program created a mini-AI module with an AI Playground assignment, giving students hands-on experience interacting with AI, understanding its capabilities, and applying it ethically and responsibly (Walter, 2024). 

AI can also support teaching in several ways. First, AI can assist students with capstone research projects by acting as a brainstorming tool, helping them generate ideas and refine their topics. For instructors in programs where students come from diverse academic backgrounds and interests, it is impossible to be an expert in every field. However, encouraging students to ask AI about current issues in their field can spark meaningful project ideas (Bowen & Watson, 2024).

Second, AI enables more timely feedback by quickly verifying sources and identifying inaccuracies on Works Cited pages. Finally, AI offers personalized feedback through tools like vMock that provide tailored insights on resumes and other documents, helping students strengthen their work with specific suggestions (Hargrave et al., 2025). 

Practical Implementation Strategies

Content Creation and Enhancement – Generative AI technologies can streamline faculty workflows by producing initial content frameworks and enhancing presentation quality across multiple educational contexts (Bowen & Watson, 2024). These applications extend to developing supplementary learning materials including study guides and multimedia content (Hargrave et al., 2025). 

Research Support – Academic research processes benefit from AI assistance in locating, validating, and organizing scholarly sources, though human judgment and scholarly responsibility must supersede AI outputs, with researchers maintaining accountability for accuracy and intellectual integrity (Hwang et al., 2020; Walter, 2024). 

Table 1 provides specific implementation strategies and practical applications across seven key educational domains: 

Table 1: AI Tools for Education (Hargrave et al., 2025; Bowen & Watson, 2024) 

Ethical Considerations for AI Implementation

Data Privacy and Bias

AI systems in educational contexts must be designed with robust protections for student data in keeping with the Family Educational Rights and Privacy Act (FERPA), (U.S. Department of Education, 2021). while actively minimizing algorithmic bias (Singh et al., 2025). Collaborative development between educational technologists and pedagogical experts can help ensure AI applications maintain fairness and reliability across diverse student populations (Cope & Kalantzis, 2019). 

Technological Disparities

Implementing educational AI faces significant obstacles. The high cost of generative AI licenses and infrastructure creates barriers for many higher education institutions seeking to integrate these technologies. This forces universities to choose between comprehensive AI access and fiscal responsibility, risking a two-tiered system where well-funded institutions offer cutting-edge AI education while resource-constrained schools fall behind. 

Beyond cost-related concerns, the implementation of AI tools in the classroom presents two interrelated challenges. First, faculty members exhibit varying levels of technological readiness, necessitating intentional support structures that combine accessible tools with sustained, high-quality professional development (Bowen & Watson, 2024). Without such scaffolding, even well-designed AI interventions risk uneven adoption and limited pedagogical impact. Similarly, disparities in students’ technological literacy can lead to inequitable learning experiences. To address this, the integration of foundational AI literacy modules can help establish a shared baseline of competence, while also offering experiential learning opportunities that demystify the role of AI in academic work (Kadry, 2024). 

Future Opportunities

Emerging AI trends suggest a trajectory in higher education toward increasingly personalized, collaborative, and adaptive learning environments (Shah, 2023). Current technologies offer individualized learning pathways, constructive feedback mechanisms, and curriculum development support that enhance teaching effectiveness. As AI capabilities advance, dynamic curriculum adjustments may further personalize educational experiences (Hwang et al., 2020). 

Persistent challenges include faculty concerns regarding assessment authenticity and the cultivation of independent critical thinking (Ryzheva et al., 2024). Implementation also presents practical considerations around curriculum revision timelines and intellectual property questions. Ongoing research and thoughtful integration remain essential to maximize benefits while addressing these legitimate concerns (Milli, n.d.). 

Key Findings on AI in Education 

Current Benefits of AI: AI offers personalized learning paths, instant feedback, and enhances teaching efficiency through tools that assist in curriculum adjustments and content creation (Bowen & Watson, 2024). 

Future Possibilities: There is potential for AI to evolve into dynamic, adaptive curriculum adjustments, which could further enhance personalized education (Hwang et al., 2020). 

Ethical Concerns: Educators face challenges in maintaining authentic assessments and ensuring students develop independent problem-solving skills amidst AI integration (Ryzheva et al., 2024). At the same time, they must address varying levels of technological readiness among faculty and students, which can create implementation gaps and lead to inequitable access to AI-enhanced learning opportunities. Concerns about data privacy and security also emerge as institutions navigate the collection, storage, and use of student data by AI platforms. These challenges require clear policies and safeguards to protect sensitive information while enabling effective AI integration (Singh et al., 2025). Such concerns are especially pressing in regions governed by legal frameworks like FERPA, which mandates strict protections for student data and limits how educational institutions can share or use that information (U.S. Department of Education, 2021). 

Implementation Challenges: Some educators express concerns about the workload of rewriting curricula and the infringement of intellectual property in user tests (Walter, 2024). Conclusion

Conclusion

AI in education offers promising innovations that can transform teaching and learning. AI tools make learning more engaging and efficient (Kadry, 2024). By emphasizing the importance of human judgment and experience, fostering a collaborative environment where AI is used as a tool to support, not replacing human decision-making, and addressing ethical considerations, educators can create more efficient and personalized educational experiences (Bowen & Watson, 2024). Future research should focus on developing inclusive and fair AI systems that help all stakeholders (Singh et al., 2025).  

References

Bowen, J. A., & Watson, C. E. (2024). Teaching with AI: A practical guide to a new era of human learning. JHU Press. 

Cope, B., & Kalantzis, M. (2019). Education 2.0: Artificial Intelligence and the end of the test. Beijing International Review of Education, 1(2-3), 528-543. https://doi.org/10.1163/25902539-00102009 

Hargrave, M., Fisher, D., & Frey, N. (2025). The artificial intelligence playbook: Time-saving tools for teachers that make learning more engaging. Corwin Press. 

Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001 

Kadry, S. (Ed.). (2024). Artificial intelligence and education – Shaping the future of learning. IntechOpen. https://doi.org/10.5772/intechopen.1002135 

Milli, M. (n.d.). Ethical dimensions of artificial intelligence balancing innovation and responsibility. In AI and emerging technologies (pp. 161-183). CRC Press. 

Ryzheva, N., Nefodov, D., Romanyuk, S., Marynchenko, H., & Kudla, M. (2024). Artificial intelligence in higher education: Opportunities and challenges. Amazonia Investiga, 13(73), 284-296. 

Shah, P. (2023). AI and the future of education: Teaching in the age of artificial intelligence. John Wiley & Sons. 

Singh, A., Lakhera, G., Ojha, M., Kumar Mishra, A., & Nain, A. (2025). Balancing innovation with responsibility: Ethical dimensions of AI in revolutionizing e-learning. In Ethical dimensions of AI development (pp. 467-500). IGI Global. 

Treatment.com AI Inc. (2024). How AI is transforming medical education for the next generation of doctors. https://www.treatment.com/blogs/how-ai-is-transforming-medical-education-for-the-next-generation-of-doctors/ 

Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21, 15. https://doi.org/10.1186/s41239-024-00448-3  

Authors

Patricia Farless, Senior Instructor, Department of History, College of Arts and Humanities, University of Central Florida   

Katia Ferdowsi, Lecturer, Department of Health Sciences, College of Health Professions and Sciences, University of Central Florida 

Karen Haslett, Lecturer, Interdisciplinary Studies, College of Undergraduate Studies, University of Central Florida  

Jo Smith, Instructional Designer, Center for Distributed Learning, University of Central Florida

Florence Williams, Senior Instructional Designer, Center for Distributed Learning, University of Central Florida