Research Insights #1: Experimenting with analzying research literature

Leveraging generative AI to get through that TBR folder

This is a new series that I’m doing here where I explore more directly some of the research out there related to education (usually with at least 1 or more articles focused on generative AI) and use generative AI to help me do it. In a post later this week, I’ll walk through the process I did to do this along with the prompts used.

Disclaimers

1. I will always make clear what is from me and what is from ChatGPT. My intention is to have the ChatGPT to review and analyze the article along certain elements and then to reflect and write about what I’m seeing.

2. This is not as good or reliable as reading the actual research directly. I agree 100%. It’s probably more akin to skimming. Still, to me, this approach gets me thinking about the content in the article that reading it doesn’t. Because nearly decades of collecting articles shows me one single fact. 99% of them I am never going to read or open again. If this as a practice makes some of the research more accessible to me, then I am probably the better for it.

3. This is an experiment as a series to see if it is valuable both to me and readers. Feedback is always welcomed.

Image created with DALL-E in ChatGPT 4 with prompt: “create an image of old and contemporary books swirling around the head of a cybernetic head whose eyes are alight with blue glow and is downloading the knowledge from the books into its brain”

Article 1

The following is an AI-generated summary and assessment of the article: Farrelly, T., & Baker, N. (2023). Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice. Education Sciences, 13(11), 1109.

• Chat link

• Online link

Overview

• Objective: Exploring the impact of Generative Artificial Intelligence (GAI) on higher education, focusing on student life and international students.

• Audience: Educators, policymakers, researchers in higher education, and those at the intersection of technology and education.

Key Insights and Recommendations

• Generative AI in Education: GAI's transformative potential in education is examined, with emphasis on revolutionizing student life and academics.

• Academic Integrity Concerns: The article delves into the challenges of detecting AI-generated content in academic work and the risks of false accusations, especially towards international students.

• Support for International Students: GAI is highlighted as a potential personal tutor and language support tool, advocating for equitable access to technology.

• AI Literacy and Ethical Engagement: Emphasis is placed on integrating AI literacy in curricula, fostering a critical understanding of AI's capabilities, limitations, and ethical considerations.

• Practical Application in Teaching and Learning: Recommendations include the thoughtful incorporation of GAI in pedagogy, using AI for personalized learning, language support, and supplemental educational tools.

• AI for Diverse Learning Needs: The article suggests leveraging AI to support diverse learners, including those with disabilities, through language translation and personalized experiences.

Challenges and Unconventional Approaches

• Detection of AI-Generated Content: Identifying AI-generated content poses significant challenges, raising concerns about academic integrity.

• Bias and Equity in AI: The article underscores biases in AI systems and the need for equitable AI usage that doesn't perpetuate existing inequalities.

• Rapid Technological Evolution: The fast-paced evolution of GAI technologies presents a continuous adaptation challenge for educators and institutions.

• Use of GAI as Personal Tutors for International Students: An innovative approach suggesting personalized, language-sensitive support systems for international students.

• Ethical and Critical AI Engagement: Advocates for a comprehensive understanding and critical analysis of AI technologies in educational contexts.

• AI as a Tool for Enhancing Accessibility: Proposes AI as an active assistant in breaking down learning barriers, especially for students with disabilities.

Overall Assessment

• Rating: 7.8/10

• Strengths: Well-researched, balancing theoretical insights with practical considerations, offers valuable insights for educational development.

• Areas for Improvement: Greater diversity in perspectives and more accessible writing style for broader audiences.

Keywords for Categorization

• Generative Artificial Intelligence (GAI)

• Academic Integrity

• AI Literacy in Education

• Support for International Students

• Ethical Engagement with AI

• AI and Accessibility

• AI in Pedagogy

• Technological Evolution in Higher Education

My Response to Article 1

Overall, this article doesn't appear to offer a whole lot that isn't already out there. Things that do stick out that seem interesting to further pursue include the discussion of international studies and the role of generative AI in support. It intersects with the idea of AI for Diverse Learning Needs in trying to leverage these generative AI tools to provide more support to students who typically are not well-supported in higher education. I do appreciate the concerns and challenges that the article discusses as they seem largely aligned with the discussions I've seen. As an exploration into the "implications and considerations", I feel like the summary feels as effective for me as the article but I'm also more deeply knowledgeable about the field. Therefore, this article could be the thing I refer someone else to who is just trying to wrap their heads around the topic.

Article 2

The following is a AI-generated summary and assessment of the article: Lazaroiu, G., & Rogalska, E. (2023). How generative artificial intelligence technologies shape partial job displacement and labor productivity growth. Oeconomia Copernicana, 14(3), 703-706.

• Chat link

• Online link

Overview and Assessment

• Objective: Analyze the impact of GAI on labor markets, with a focus on job displacement and labor productivity growth.

• Audience: Economists, labor market analysts, technology experts, and policy makers.

• Overall Rating: 7.8/10 for its comprehensive analysis and practical insights, though with a need for broader perspectives and accessibility.

Key Insights and Applicability to Education

• Impact on Labor Markets: The article details how GAI is reshaping labor markets, emphasizing job displacement and productivity.

• Reconfiguring Professional Roles: Highlights the transformation of managerial and professional roles due to GAI, relevant for educators in preparing students for future careers.

• Enhancement of Labor Productivity: Discusses GAI's role in improving operational efficiency and workforce development, offering insights for curriculum development in education.

• Workforce Development and Talent Management: GAI's influence on talent management is a crucial aspect for educators, particularly in career counseling and vocational training.

• Applicability: These insights are valuable for educational developers and faculty in understanding labor market dynamics and integrating AI literacy and relevant skills into their curricula.

Methodologies and Theoretical Perspectives

• Data-Driven Approach: Employs quantitative data analysis and case studies to explore labor dynamics influenced by GAI.

• Interdisciplinary Insights: Combines economic theories with technological insights, presenting a comprehensive view of GAI's impact.

Surprising and Unconventional Approaches

• GAI in Managerial and Professional Development: The extent of GAI's influence on traditional roles is unexpected, suggesting a shift in educational focus to AI-centric competencies.

• AI-Driven Workforce Analytics: The use of GAI for workforce analysis and development offers a novel perspective for educational strategies.

Challenges and Limitations

• Limited Scope on Emerging Economies: The focus is primarily on developed economies, which may limit its applicability globally.

• Rapid Technological Evolution: The fast-paced advancement of GAI technologies presents a continuous challenge for updating educational content.

Keywords for Categorization

• Generative Artificial Intelligence (GAI)

• Labor Productivity Growth

• Job Displacement

• Workforce Development

• AI in Talent Management

• Reskilling and Upskilling

• Labor Market Dynamics

• AI-Driven Workforce Analytics

Response to Article 2

I randomly selected articles for this activity as I'm figuring it out as a process. This wouldn't have been an article I would have typically included for two reasons. The first is that it is not the same topic as the other two and ideally, I would group 3 articles of overlapping relevance together for larger insight. Additionally, this is a short piece (3 pages) and editorial. Which is interesting because the generative AI does not pick up on it and I had to go back and look at the original article. So refining this process will need to continue to happen (more on that in the next post).

Article 3

The following is a AI-generated summary and assessment of the article: Pechenkina, K. (2023). Artificial intelligence for good? Challenges and possibilities of AI in higher education from a data justice perspective. Higher Education for Good: Teaching and Learning Futures (# HE4Good), Open Book, Cambridge, UK.

• Chat link

• Online link

Overview and Assessment

• Objective: Investigating the ethical application of AI in higher education, focusing on data justice, equity, and privacy.

• Target Audience: Educational developers, policy makers, researchers at the intersection of technology, ethics, and education.

• Overall Rating: 7.3/10 for its in-depth analysis and unique focus on data justice and ethical considerations, although it demands a certain level of prior knowledge from the reader.

Key Insights and Findings

• Ethical and Equitable Use of AI: The article centers on the ethical implications of AI in education, advocating for data justice and addressing equity and privacy concerns.

• AI's Role in Education: Discusses AI's integration in educational settings, including administrative tasks, student feedback, and predictive algorithms.

• Data Justice Perspective: Emphasizes a data justice framework, advocating for fair and equitable AI usage that respects digital rights and privacy.

Applicability to Education

• Curriculum and Teaching Practice Implications: Offers insights for curriculum development and teaching practices, emphasizing ethical AI usage and data justice principles.

• Awareness of Ethical Challenges: Encourages educators to critically engage with AI technologies, considering their implications on equity and justice.

Unconventional Findings and Approaches

• Data Justice Focus: The novel emphasis on data justice in AI applications in higher education.

• Ethical Dilemmas in AI Surveillance: Highlights the ethical concerns with AI-driven surveillance in educational settings.

• Pedagogically-Led AI Implementation: Stresses the alignment of AI tools with educational theories and pedagogical principles.

• Agency in AI Usage: Advocates for agency, active governance, and accountability in AI usage in education.

Keywords for Categorization

• Data Justice in Education

• AI Ethics in Higher Education

• AI-Driven Surveillance

• Digital Rights and Privacy

• Pedagogical AI Implementation

• Equity and AI in Education

• Ethical AI Policy

• AI and Academic Integrity

Response to Article 3

I feel that this summary does give me some deeper appreciation of the article and wanting to know more. It would lead me to ask more questions about the findings and concrete practices to consider. It certainly highlights some valuable considerations for educators and educational developers that I have seen as part of this discussion and at times, seems absence in the discussion of generative AI in education.

Final Thoughts

I tried this out on 3 articles: one about possibilities and concerns, an editorial about workforce implications, and a consideration of data justice. I don't quite feel like I have learned much about the content itself in this process but more like just better refined and applicable abstracts for my own point of reference. And that's potentially useful as this process may be more about building a personalized library with clarity about relevant research to follow up as needed. I'll talk more about this in the next post of how I build this out and what I hope to do with it from here. But for now, I do still think it could be helpful for folks here to see and learn more about the research that is out there about generative AI and getting a breakdown of it in case it is relevant to their interests and fields.

I feel like 2-3 articles per post makes sense. I'm leaning towards making sure they are directly connected and then adding a final question about how they all connect and intersect. In fact, I did that with this group as well and this is the generative AI output I got from that question.

Interesting Connections Across Articles:

1. Ethical and Social Justice Implications of AI:

   • All articles emphasize the ethical considerations and potential social justice implications of AI. Whether discussing AI in the labor market or in higher education, the need for fairness, equity, and data justice is a recurring theme. This highlights the broader societal impact of AI technologies beyond their immediate functional applications.

2. AI Literacy and Critical Engagement:

   • A common thread is the importance of AI literacy. Educators are encouraged to develop not only their understanding of AI technologies but also to critically engage with these tools, understanding their limitations, biases, and potential ethical issues.

3. Impact on Job Roles and Skills:

   • The articles collectively underscore the transformative impact of AI on job roles and the skills required in the workforce. This transformation necessitates a reevaluation of curricular content and pedagogical strategies to prepare students for an AI-influenced economy.

4. Challenges of Rapid Technological Evolution:

   • A consistent challenge mentioned is the rapid pace of AI technology development. This poses a significant issue for educators and educational developers in keeping curricula, teaching methods, and policy considerations up to date.

5. AI as a Tool for Inclusivity:

   • The potential of AI to enhance inclusivity and accessibility in education, especially for language learning and students with disabilities, is highlighted. This presents an opportunity for AI to contribute positively to diverse educational environments.

Application of Findings for Educational Developers and Educators:

1. Integrating AI Ethics into Curricula:

   • Develop courses or modules focused on AI ethics, data justice, and the social implications of AI. This could be part of a broader digital literacy initiative or integrated into existing subjects.

2. Fostering AI Literacy Among Students and Faculty:

   • Host workshops or seminars to educate students and faculty about AI, its uses, biases, and limitations. This would help in creating a more informed and critically engaged user base within educational institutions.

3. Curriculum Development Reflecting Job Market Changes:

   • Redesign curricula to include skills and knowledge relevant to an AI-driven economy. This could involve new courses in data science, AI, and machine learning, or the integration of AI-related content into existing courses.

4. Using AI for Personalized Learning and Accessibility:

   • Implement AI tools to support personalized learning experiences and to assist students with disabilities. This could include AI-powered tutoring systems or adaptive learning platforms.

5. Staying Updated with Rapid Technological Advances:

   • Establish continuous professional development programs for educators to stay current with the latest AI advancements and pedagogical strategies.

6. Promoting Interdisciplinary Collaboration:

   • Encourage collaboration between different disciplines (e.g., education, technology, ethics) to foster a more holistic understanding of AI's role and impact in education.

So what are your thoughts? Is something like this useful?

AI+Edu=Simplified by Lance Eaton is licensed under Attribution-ShareAlike 4.0 International