What about AI?

I think AI can be a bit overwhelming.  That is why I started this blog.  I also think I have pretty average tech skills.  The parts I know, I know really well, but mostly I know what can be done, not necessarily how to do it. I am also really good at watching YouTube tutorials.  A lot of us fall into that category.  

Well, AI is another tool in that toolbox.  The general, generative AIs, like Gemini or ChatGPT, are super easy to use and the responses are almost conversational.  When you are struggling to get Google to understand what you are asking for, try an AI.  Small bites are always good.  Instead of doing a bunch of research for my Heritage Trail windows, I asked it to name two fun things to do in every county in the Pecos Trail region.  It did exactly that.  If you want to know what they were, read VP Arca's weekly newsletter for my More than Just Books articles. 

These little things are a great way to familiarize yourself with using AI and learning all the ways they can help, without making you feel like you are cheating.

Regular readers know that I think Consensus is a great tool for college students to be guided to for research.  Recently, they added a search history page for those who are logged in when they do their searches.  I don’t know how organized you are, but I find myself doing the same searches multiple times when I am working on a big project.

For our Allied Health department, Consensus searches now support MeSh synonyms to improve the accuracy of medical queries.  Remember, all of PubMed is included in Consensus, plus many other related resources.  Now you can get it all in one place.

As most of my regular readers know, I read widely on AI subjects and very often find things I want to share.  This week's post comes from David Moldawer’s blog, The Maven Game, and his post, The Power of a Blue Book.  Very much of what he said is what I wish I had thought of myself, but like the cheating mentioned in his post, I am letting someone else do my thinking for me here. 

What we have to recognize is that students are going to use AI, they are going to use it to cheat, and the detectors we use provide so many false positives that they are almost useless. The battle against LLM abuse by students is already lost.  So what can we do instead? 

First, acknowledge the long-known fact that writing is a significant part of learning.  Creating a sentence is hard work and creating multiple sentences that go together coherently is really hard.  If we let LLMs do the writing, we are doing a disservice to them and us. 

Second, we have to acknowledge that the only solution is to write.  In the classroom, during class time, create assignments that require them to think and put it on paper.  This isn’t easy, I know.  We already have too much to cover and too little time to do it, but we also sacrifice hard assignments for simpler things that can be easily graded by Canvas.  Students need hard assignments to learn and we have to grade them. 

I thought I was doing this for my History 2321/2 classes at Dallas College.  The plan was to spend the first ten minutes of each class having the students write a summary of the readings they were supposed to have done before class.  I was a new teacher and let it go a little awry.  They would come to class with their book, reading for highlights and copying them down. At first, I let them, thinking that at least they were looking at the book.  Soon, though, I had allowed a pattern to be established and I did not know how to break it.  Grading wasn’t much work.  There was never more than half a page.  I could look at it for a few seconds and catch anything wrong.  It was pretty much a matter of ticking off the assignments as complete.  Now, if I had not allowed books, they would have had to think for themselves, they would have gotten in the habit of doing the reading before class, and the grading would not have been any harder.  Yeah, I will do that next time.  You could try it.

Consensus was great when it provided abstract information for relevant journal articles, but they have updated their access, and now you get search results that pull straight from the full text of the papers.  In some ways, this will be great.  We all know abstracts rarely provide the information you want.  With the new full-text access, you won’t have to search through a 120 page article to find the relevant parts.  Consensus does that for you.  Of course, it is only a program and is working from your prompts, so some tweaking will definitely be required.  It is also a good idea to pull the full articles to make sure the AI did not miss anything, but this is another tool that will save you enough time to make it worthwhile.

Data Analysis and Prediction

On the surface, these may not seem like student tasks, but analyzing statistics and being able to make informed choices based on your findings is exactly what students should be able to do.

• Tasks: Extracting insights from data, identifying patterns, and forecasting future trends.
  • Predictive Analytics:  Forecasting future events (e.g., customer churn, sales forecasting, fraud detection).
  • Data Cleaning and Preparation:  Automating the process of making data ready for analysis.
  • Anomaly Detection:  Identifying unusual patterns or outliers in data (e.g., fraudulent transactions).
  • Business Intelligence (BI) Augmentation:  Enhancing traditional BI with AI-driven insights.
  • Risk Modeling:  Assessing and quantifying financial or operational risks.

• Programs/Platforms:
  • Google Cloud AI Platform (Vertex AI, BigQuery ML): Comprehensive suite for building, deploying, and managing ML models, including predictive analytics.  These are really big picture programs for creating entirely new AI programs.       
  • AWS SageMaker:  Fully managed service for building, training, and deploying machine learning models.
  • Azure Machine Learning: Microsoft's cloud-based platform for ML.
  • TensorFlow & PyTorch:  Underlying frameworks for building custom predictive models.
  • R:  Statistical programming language widely used for data analysis and machine learning.
  • Databricks:  Data platform that integrates AI/ML capabilities for large-scale data analysis.

Natural Language Processing (NLP)

These are your big, broad tools that do just about anything.  This is where good prompting skills come into play.

• Tasks: Understanding, interpreting, and generating human language.  This includes:
  • Text Summarization:  Condensing long texts into shorter, coherent summaries.
  • Sentiment Analysis:  Determining the emotional tone of text (positive, negative, neutral).
  • Chatbots/Conversational AI:  Enabling human-like conversations, answering questions, and providing support.
  • Translation: Converting text from one language to another.
  • Named Entity Recognition (NER):  Identifying and classifying entities like names, organizations, and locations.
  • Topic Modeling:  Discovering abstract topics in a collection of documents.
  • Text Generation:  Creating new text, like articles, emails, or creative content.

• Programs/Platforms:
  • Google Cloud Natural Language API (GEMINI):  Pre-trained models for sentiment analysis, entity extraction, content classification, and more.  This is Gemini.  This is free all the time and is a pretty good tool.  I use it every day. 
  • OpenAI's GPT series (ChatGPT):  Excellent for general text generation, summarization, and conversational AI.       There is a free level of this.
  • NLTK (Natural Language Toolkit):  A popular Python library for research and development in NLP, offering tools for tokenization, stemming, tagging, parsing, and more.  Python can do anything, but it is a language you will need to learn first.

After going through the big AI answer, I realized that there is no way I will be discussing seven types.  Right off the top, I am going to drop all the programs that write AI programs.  If you have a specialty need for your business, these are great, but this blog is meant for students, staff, and faculty of a community college.  When you are in graduate school, you might need those tools. When you are ready, do an AI search, prompting it to name the tool that will best suit your task.

Another thing I dropped are multiple programs from the same company, specifically Amazon, Microsoft, and Google.  They show up in the list multiple times with different names.  As an example, Amazon has AI programs called Rekognition, Comprehend, and SageMaker and none of them are free.  Essentially, they are all the same – programs that write programs.  All Microsoft programs come with associated costs.  Google has at least nine programs.  They boil down to Gemini and a variety of prompting techniques. 

Python is an open-source programming language that is closer to human language than any other programming language.  It is really flexible and, for our purposes here, can be used to write just about anything.  It is a first-level skill you would have to acquire, like the ability to write HTML.  From there, the possibilities are endless. 

The robotics, healthcare, and financial services modeling tools are simply unrelated to our needs.  I suspected that when I got started, but decided to leave them in, thinking there might be something useful.  There probably is, but for this blog, they are just clutter.  My target audience may never need tools like that.  Once a profession is chosen, the employer will have a solution that fits their needs. 

Computer vision tools are pretty cool, but are pretty deep for our needs.  This is image manipulation, OCR reading, and facial recognition.  You may use these tools a lot in life but probably not much in school.

So, after a false start, over the next four weeks we will take a deep dive into:

1. Natural Language Processing (NLP)
2. Generative AI (Art, Music, Code, etc.)

3. Data Analysis and Prediction

   This is another reason why you should always check your AI response.  It may not fit your needs.

    

My biggest concern with AI is which program to use for what thing.  I have sort of covered that here by naming Consensus as a great AI for student research because it only searches open-source, peer-reviewed journals.  Alternatively, something like Gemini is great for proofreading and general information.  I used it yesterday to reduce a 3000-word story down to 2500 words to meet submission requirements.  The big caveat with something like that is that you have to proofread it afterward to make sure it still makes sense.  AI is a tool, not an answer. 

There are a lot of AI programs about there.  So, for the next few weeks I am going to look at some specific AI programs, sorted by type. Remember, these are being published in August 2025.  By September, they will be out of date.  Here is my big secret though – I used AI to get this information.  I searched the general internet for this information and could not find anywhere that discussed specific brands by feature. Gemini could and did with the prompt “Which AI programs do what tasks?”  I imagine there are a lot missed, but I did look each one up so I could provide a hyperlink. At the very least, everything I include in these next few discussions actually exists.  Additionally, I removed every program that does not have some level of free use. 

Gemini tells me there are seven basic types of AI:

1. Natural Language Processing (NLP)
2. Computer Vision (CV)
3. Generative AI (Art, Music, Code, etc.)
4. Data Analysis and Prediction
5. Robotics and Automation
6. Healthcare Applications
7. Financial Modeling

Over the next few weeks, we will take an in-depth look at each of these, with a heavy emphasis on programs that will most likely help with schoolwork.  We will start next week with Natural Language Processing, the biggest group.

When Chat GPT broke over the world three years ago, I did not know anything more about AI than any other ordinary person.  I did know that it was important and it could seriously change the world as we know it. I also immediately saw its potential for students to use it to do their homework for them.  That is the primary thing I have been studying and learning since the beginning.  There are a lot of really useful ways for everyone to use AI.  Students have to remember, though, that they are learning and sometimes you just have to do things the hard way in order to make them stick. 

One of the places I get my information is a blog called One Useful Thing by Ethan Mollick.  Earlier this month, he published a blog called Against ‘Brain Damage’ exploring how AI can help or hurt the way we think.  It had a lot of useful information and I may revisit some of the topics later, but today I want to look at a specific study.

According to Mollick, researchers “at Penn conducted an experiment at a high school in Turkey where some students were given access to GPT-4 to help with homework.  When they were told to use ChatGPT without guidance or special prompting, they ended up taking a shortcut and getting answers.  So even though students thought they learned a lot from ChatGPT's help, they actually learned less - scoring 17% worse on their final exam (compared to students who didn't use ChatGPT).”

The same study surveyed the students four months later and found students who had used Chat GPT remembered less than students who did not use AI for the same assignment.  What it boils down to is that getting the answers is not the same as finding the answers.  Do the work, do the research.  I am able to pull information from papers I wrote twenty or more years ago out of my memory because I had to learn the material as I was working on it. You can (and should) too!