Overview

In the rapidly evolving landscape of higher education, Artificial Intelligence (AI) has emerged as a powerful tool for transforming academic content delivery, research, and assessment. This mini- course, "Integrating AI for Summarizing and Analyzing Academic Content in Higher Education," provides faculty with a comprehensive guide to harnessing AI's potential in their professional workflows.

Key Objectives

• Utilize AI Tools for Content Summarization

• Apply AI Tools for Content Analysis

• Integrate AI into Academic Grading and Assessment: Explore the application of AI in grading and providing feedback on student submissions.

• Implement AI Insights to Improve Teaching and Learning: Translate AI analyses into actionable improvements for course content and teaching strategies.

• Foster a Data-Driven Academic Environment: Encourage the adoption of AI technologies to enhance educational efficiency and impact.

Introduction

AI has rapidly transformed various industries, including education. It helps streamline research, personalize learning, and assist in grading and assessment tasks. AI's capabilities help educators condense academic content, analyze research, and evaluate student work more efficiently. This allows more time for direct student engagement and other teaching tasks.

1. Content Summarization:
   • Text Summarization Concepts: AI summarization falls into two main types:
     • Extraction-based: AI selects key sentences or phrases from the original text to create a summary, often based on metrics like word frequency or sentence relevance.
     • Abstraction-based: AI generates new text by interpreting the original content, producing summaries that are not limited to existing sentences.
   • Practical Uses: Summarization can simplify academic papers, research articles, and course materials:
     • Academic Papers: Summaries provide quick overviews, helping researchers decide if a paper is relevant.
     • Course Materials: Summaries can turn lecture notes or chapters into condensed guides, assisting students with revision and understanding.
2. Content Analysis:
   • NLP Techniques:
     • Sentiment Analysis: Categorizes text into positive, negative, or neutral sentiments, offering insights into student feedback or research trends.
     • Entity Recognition: Identifies entities like names, organizations, or concepts, which can map relationships in academic content.
     • Thematic Analysis: Identifies recurring themes or topics, providing insights into research trends or student understanding.
   • Applications in Research:
     • Research Trends: NLP can analyze collections of academic papers, revealing emerging topics and shifts in discourse.
     • Course Improvement: NLP can analyze student feedback or evaluations to identify recurring themes, guiding course adjustments.

Practical AI Tools for Summarizing and Analyzing Academic Content

Example: Content Summarization and Analysis for Research

• Example Prompt: "Summarize the following text: [Paste Text Here]."
• Using a PDF Extractor and/or a text splitter: When the content exceeds the ChatGPT character limit or is within a PDF, you can use free programs to extract the text from the PDF and submit it in usable chunks into ChatGPT
  • PDF to Text Converters: There are various online tools and software that can convert PDF files into plain text. Tools like Adobe Acrobat Reader, Smallpdf, or PDFtoText.com can extract text from PDFs effectively.

• Long PROMPTs Splitter (jjdiaz.dev): See an example of how to use this along with ChatGPT at https://chat.openai.com/share/64fa719b-d3bc-4783-9dbc-9c07ba629ea8 

1. • Customized Prompts: After you have the text that you want to summarize either ready and/or summarized, there are some tips on how to refine prompts to produce tailored summaries:
     • "Summarize the following text with a focus on its main arguments."
     • "Provide a 3-sentence summary of the following text."
     • “Summarize the follow text within the context of SoTL.”
   • You can also them combine the thread of summaries and ask for a larger review of the generated summaries and consider how they can be applied to teaching or research tasks.

Analysis within ChatGPT:

Follow the instructions above for adding the text then:

• Thematic Analysis: 
  • "What are the main themes in the following text?"
• Sentiment Analysis: Input articles, essays, survey results for sentiment analysis:
  • "Analyze the sentiment of the following text."

Further Example: Content Summarization and Analysis with AI for Grading and Assessment 

AI can analyze student submissions, generating thematic and sentiment insights, highlighting class-wide trends, and identifying areas where course materials may need adjustment.

1. Class-Wide Trends:
   • Insights: AI can generate reports on student performance, identifying class-wide trends:
     • Themes: Thematic analysis can reveal trends in student understanding, highlighting which topics are clear or require further clarification.
     • Sentiments: Sentiment analysis can show shifts in student attitudes over time, guiding adjustments to teaching strategies.
   • Actionable Changes: You can apply these insights to refine teaching strategies or course materials, addressing any recurring issues or gaps in understanding.

Scenario: Using AI to Evaluate Student Essays in a Literature Course

A university literature professor decides to use an AI tool to assist in grading student essays on Shakespeare's "Hamlet." The primary goal is to obtain a deeper understanding of student comprehension and engagement with the material, as well as to streamline the grading process.

Implementation of AI in Assignment Evaluation:

1. Assignment Submission:

   • Students submit their essays through the university's learning management system (LMS), which is integrated with an AI grading tool.

2. AI Analysis:

   • The AI tool processes each essay and performs thematic and sentiment analysis.
   • Thematic Analysis: The AI identifies major themes discussed in the essays, such as "revenge," "madness," and "political intrigue," and assesses how well each student has understood and elaborated on these themes.
   • Sentiment Analysis: The AI evaluates the tone and sentiment of the essays to determine students' engagement levels and emotional responses to the play’s characters and plot developments.

3. Feedback Generation:

   • The AI tool generates feedback for each essay, highlighting strengths (e.g., good comprehension of the theme of "revenge") and areas for improvement (e.g., insufficient analysis of "political intrigue"). This feedback is based on the depth of analysis and relevance of content linked to the course objectives.

4. Class-Wide Trends:

   • After analyzing all submissions, the AI tool aggregates the data to identify class-wide trends. For example, it might reveal that 70% of the class grasped the theme of "revenge" very well but only 30% effectively discussed "political intrigue."
   • The tool also provides sentiment analysis results indicating that students felt strongly positive about the character development but were confused about the historical context.

5. Insights for Course Material Adjustment:

   • Based on the AI’s findings, the professor notices that while the students are connecting well with character-driven themes, there is a gap in understanding the historical and political context of the play.
   • To address this, the professor decides to adjust the course discussions by including more resources on Elizabethan politics and its depiction in Shakespeare’s works.

6. Refining Teaching Strategies:

   • Encouraged by the insights provided by sentiment analysis, the professor plans to incorporate more interactive discussions and debates around character motivations and historical settings to enhance student engagement and comprehension.

Conclusion

By regularly applying AI-generated insights, faculty can refine content delivery and assessment methods, enhancing both their teaching strategies and students' learning experiences, fostering continuous improvement in the academic environment.

Developed by Morgan McNaughton, Grand Canyon University

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