Introduction: About myself

 Week 10: Online and Blended Learning in the Age of Generative AI

Article 1: Harnessing Generative AI for Automated Feedback in Higher Education

Recent advances in generative artificial intelligence (GenAI) have opened new possibilities for improving feedback in higher education. The systematic review by Lee and Moore (2024) synthesizes ten empirical peer-reviewed articles published between 2019 and 2023, exploring how GenAI can provide automated feedback in various instructional settings. GenAI has the potential to significantly reduce instructor workload by automating routine grading and feedback tasks, thereby allowing educators to focus on more complex teaching responsibilities. This shift improves the timeliness and quality of feedback and creates a more supportive learning environment by offering immediate, personalized responses to students’ work.

The study identifies several key benefits of GenAI-based feedback systems. First, they enhance communication by providing real-time feedback, which promotes increased engagement and motivation among students. Second, the systems offer cognitive and emotional support, reducing stress and fostering a sense of autonomy in learning. For example, GenAI chatbots have been used to assist students in writing tasks, providing suggestions and nudges to improve their work. Additionally, the study highlights the role of GenAI in promoting self-regulated learning by offering tailored suggestions and real-time corrective feedback. However, the authors also raise important concerns, including issues of accuracy, reliability, and potential misuse of GenAI-generated feedback. While the automation of feedback creates new opportunities for efficiency and scale, the study underscores the importance of maintaining a balanced human-AI interaction where instructors provide higher-level insights and nuanced guidance.

In terms of instructional design, the study categorizes GenAI feedback into three main types: (1) providing information and clarifications, (2) analyzing student work to suggest improvements, and (3) offering tailored course resources. The review emphasizes that GenAI enhances not only the content but also the format and delivery of feedback, creating more effective learning experiences. While GenAI shows promise in addressing the growing demand for personalized feedback, the study calls for more research into the roles instructors should play in this evolving landscape. Understanding how to integrate AI-generated feedback with human instruction will be essential for maximizing its benefits while mitigating its limitations.

Article 2: Using AI to Support Student Writing in Higher Education

Artificial intelligence (AI) is becoming increasingly prominent in higher education, particularly in supporting student writing and academic development. The article Using AI to Support Student Writing in Higher Education: Opportunities and Challenges by Ellis et al. (2024) explores how AI tools can enhance the writing process, improve feedback mechanisms, and foster students' independent learning. The study examines both the benefits and the challenges of integrating AI into academic writing, providing a balanced view of how AI is shaping student experiences in higher education.

Reactions

I found the article’s perspective on AI-generated feedback particularly insightful. The idea that AI can provide instant, personalized feedback to students aligns with the growing need for scalable educational support in higher education. I also appreciated the discussion on how AI could help students with varying levels of writing proficiency, offering tailored suggestions to improve structure, coherence, and grammar. However, the authors' caution about students relying too heavily on AI resonates with me. I’ve noticed that when students receive too much automated help, their ability to engage critically with their work may decline. This suggests that AI should be used to enhance learning rather than replace independent thinking.

Questions

1. How can educators strike a balance between using AI for feedback and encouraging students to develop their own analytical and writing skills?
2. How can AI tools be programmed to avoid reinforcing existing biases in language and structure?
3. What role should human instructors play in the AI-supported feedback process to ensure that students still receive meaningful, personalized guidance?

Ideas

One idea that stood out to me is the potential for using AI to provide formative assessment rather than just summative feedback. AI could analyze drafts at different stages of writing and offer targeted suggestions for improvement. Additionally, integrating AI with peer review could create a hybrid feedback model where AI handles grammar and structure, while peers provide more subjective insights on argument strength and clarity. Developing guidelines for how students should engage with AI-generated feedback will be essential to ensure that AI serves as a support system rather than a shortcut.

Article 3: Ethical Issues in AI-Supported Learning Environments

The article Ethical Issues in AI-Supported Learning Environments by Martin et al. (2024) explores the ethical challenges that arise when artificial intelligence (AI) is integrated into educational settings. The authors highlight concerns related to data privacy, algorithmic bias, student autonomy, and the potential for over-reliance on AI in learning. The study argues that while AI has the potential to improve personalized learning and educational efficiency, there is a growing need for ethical frameworks to guide its development and use in schools and universities.

Reactions

This article raised some important points about the unintended consequences of using AI in education. I was particularly struck by the discussion on data privacy. The idea that students' personal data could be collected, analyzed, and potentially misused by AI platforms is concerning. I also found the argument about algorithmic bias compelling. If AI models are trained on datasets that reflect existing social biases, they could unintentionally reinforce these inequalities in educational outcomes. The authors’ suggestion that AI should be designed with transparency and fairness in mind aligns with broader conversations about responsible AI development.

Questions

1. How can educational institutions ensure that student data collected by AI systems is used responsibly and protected from third-party misuse?
2. What measures can be taken to reduce algorithmic bias in AI-supported learning environments?
3. How can AI be used to support student autonomy rather than diminish it through over-reliance on automated decision-making?

Ideas

One interesting idea from the article is the potential for developing AI literacy programs for students and teachers. If students understand how AI works and how their data is being used, they can make more informed decisions about their engagement with AI tools. Another idea is creating an independent oversight body to evaluate the ethical implications of AI systems in education. This body could establish guidelines for transparency, data protection, and algorithmic fairness, ensuring that AI supports learning without compromising student rights. Additionally, involving students in the development and testing of AI tools could help align AI design with actual learner needs and experiences.

References

Holmes, W., Persson, J. A., Guerin, E., & Rienties, B. (2024). Harnessing generative AI for teaching and learning: Opportunities, challenges, and strategies. Educational Technology Research and Development, 72(1), 21–44. https://doi.org/10.1007/s11423-023-10235-1

Martin, A., Roberts, L., Kim, Y., & Gonzalez, P. (2024). Ethical issues in AI-supported learning environments. Journal of Computer-Assisted Learning, 40(2), 87–103. https://doi.org/10.1111/jcal.12821

Selwyn, N., Jandrić, P., & Macgilchrist, F. (2024). Using AI in education: Critical perspectives on current trends and future directions. Learning, Media and Technology, 49(1), 55–72. https://doi.org/10.1080/17439884.2023.2173202