I am a participant in a book study group that covers a variety of topics. The group has an interest in writing and the writing process that is sometimes the focus of what we read and discuss. The subtopic of the preparation of content for college classes comes up frequently and sometimes the role of Open Educational Resources (OER) as classroom resources. The discussion of OER and ChatGPT (another issue that keeps coming up) got me thinking about whether ChatGPT could be wielded to produce college textbooks.
I have written a couple of college textbooks and I still dabble (see left-hand column) so I understand the process. I would not use ChatGPT for the type of course I have focused on.
I have probably taught the Introduction to Psychology course 40-50 times in my career and used maybe 10 different textbooks. Some who write for this course may disagree with my assessment that the treatment of the topics in this type of course is generic and predictable, but this would be my description. I think most efforts for this audience take a predictable approach so that the instructors making book selections are comfortable with the book they assign. I would find it easy to predict chapter themes, chapter topics, and the classic experiments that are described and related implications. I wanted to see how much I could copy and paste from the content I asked ChatGPT to generate based on my prompts and if a reasonable body of content could be produced in this fashion.
I think the best way to go about this type of writing project is to approach it as developing and then expanding an outline. You ask ChatGPT to identify topics and subtopics and then ask it to describe or explain the individual elements of the outline created in this manner. You could then ask for classic experiments, examples of supporting research, and examples of the concepts described. ChatGPT should do most of the work with this approach.
I found because I knew pretty much what I was looking for based on my existing knowledge of this type of course I could create an expanding document in which I embedded this material within the outline I created in a second tool. So think of copying and pasting material from ChatGPT to Google Docs, Word, or Scrivener. First the outline of topics and subtopics and then the explanations and descriptions, the studies, examples, and applications, and perhaps an occasional story of a famous psychologist. I don’t see creating a final product purely by copying and pasting, but I would probably write certain elements myself (e.g., chapter introductions and conclusions) and generate transitions between the chunks of the content generated by ChatGPT so the work does not seem so choppy when read. Just having the lists ChatGPT generates would save me a great amount of time in putting together such a product. I would describe the process as spending far less time thinking and more time writing and the combination of my experience and the generative capabilities of ChatGPT should allow the generation of content much more quickly and with minimal effort.
Again, this approach works because of my familiarity with the topic and the type of course I am writing to support. I could not create a credible product if writing a similar introduction to a different field of study.
What follows are some examples of the process I have just described. The idea is to offer something concrete as generated by ChatGPT. It would be far too lengthy to include all of the pieces of content for an entire chapter so I have tried to generate examples of the stages of development I propose and some of the elements to be embedded within the expandable outline I have just described.
I have selected a chapter on behaviorism as my example simply because the structure of the chapter is easy for me to conceptualize. I can begin by asking about the three main subtopics (operant conditioning, classical conditioning, and modeling) and let ChatGPT create the structure of the chapter from this initial prompt. I found it to be more difficult to approach other chapters I have experimented with, but this should be enough for a demonstration.
I will interject my own comments in the material that follows to explain what I am having ChatGPT do. The ChatGPT material appears as quotes and my prompts as bolded text.
The first set of materials provides an example of what I mean by an outline of topics and subtopics. You can see how I attempted to prompt the AI service to generate the output of the type l wanted.
Prompt: I want to have my students read about behavioral models of learning – operant conditioning, classical conditioning, and modeling. What topics are important for each model?
Sure, here are some important topics for each of the three behavioral models of learning you mentioned:
Operant Conditioning:
- Reinforcement: positive reinforcement, negative reinforcement, primary reinforcement, secondary reinforcement
- Punishment: positive punishment, negative punishment
- Shaping behavior: successive approximations, shaping schedules
- Extinction: extinction burst, spontaneous recovery
Classical Conditioning:
- Unconditioned stimulus (US)
- Unconditioned response (UR)
- Conditioned stimulus (CS)
- Conditioned response (CR)
- Acquisition: temporal contiguity, contingency, higher-order conditioning
- Extinction: spontaneous recovery, renewal, reinstatement
Modeling:
- Observational learning: attention, retention, reproduction, motivation
- Vicarious reinforcement and punishment
- Social cognitive theory: self-efficacy, outcome expectancies, personal goals
- Mirror neurons and brain regions involved in imitation and observation
The following prompt was used to expand a portion of the higher level outline in this case focused on operating conditioning.
Prompt: What should students in an introductory class know about the topics you have listed for operant conditioning?
Great question! Here are some key concepts that students in an introductory class should know about operant conditioning:
Reinforcement:
- Positive reinforcement occurs when a behavior is followed by the presentation of a pleasant stimulus, which increases the likelihood of that behavior occurring again.
- Negative reinforcement occurs when a behavior is followed by the removal of an unpleasant stimulus, which also increases the likelihood of that behavior occurring again.
Punishment:
- Positive punishment occurs when a behavior is followed by the presentation of an unpleasant stimulus, which decreases the likelihood of that behavior occurring again.
- Negative punishment occurs when a behavior is followed by the removal of a pleasant stimulus, which also decreases the likelihood of that behavior occurring again.
Shaping:
- Shaping involves reinforcing successive approximations of a desired behavior until the desired behavior is achieved.
- Shaping schedules can be continuous (every instance of the desired behavior is reinforced) or intermittent (only some instances of the desired behavior are reinforced).
Extinction:
- Extinction occurs when a behavior that was previously reinforced is no longer reinforced, which leads to a decrease in the frequency of that behavior.
- Extinction can be accompanied by an extinction burst, which is a temporary increase in the frequency of the behavior before it decreases.
It’s important for students to understand these concepts and how they apply to real-life situations, as operant conditioning plays a role in many aspects of our behavior, including education, parenting, and workplace management.
The following is a prompt intended to identify examples of reinforcement students might connect to their own lives. There would be no need to include the entire list, but I could efficiently select several from this list and expand a bit.
Prompt: What are some examples of positive and negative reinforcement might students recognize from their own experiences?
Great question! Here are some examples of positive and negative reinforcement that students might recognize from their own experiences:
Positive reinforcement
- A child receives a candy for completing their homework, which increases the likelihood of them completing their homework in the future.
- A student gets praise from their teacher for answering a question correctly, which increases the likelihood of them participating in class in the future.
- An employee receives a bonus for meeting their sales quota, which increases the likelihood of them meeting their sales quota in the future.
Negative reinforcement:
- A driver puts on their seatbelt to stop the beeping sound, which increases the likelihood of them putting on their seatbelt in the future.
- A student finishes their work early to avoid having to do it during recess, which increases the likelihood of them finishing their work early in the future.
- An employee works overtime to avoid getting a reprimand from their boss, which increases the likelihood of them working overtime in the future.
It’s important to note that reinforcement can be either positive or negative depending on whether a desirable consequence is presented or an undesirable consequence is removed. Additionally, it’s important to recognize that reinforcement doesn’t always have to involve tangible rewards or punishment; sometimes, social praise or criticism can also serve as a form of reinforcement.
The following prompts illustrates how I would identify prominent individuals associated with this topic. I would probably not include an entire list for all chapters, but it would be common to include all from this list in a chapter on this topic.
Prompt: There are several important scientists who developed our understanding of operant conditioning. Who are a few of them and some of the classic studies associated with their work
B.F. Skinner: Skinner is perhaps the most well-known and influential scientist associated with operant conditioning. He developed the “Skinner box,” a laboratory apparatus used to study operant conditioning in animals. Some of his classic studies include “The Behavior of Organisms” (1938) and “Science and Human Behavior” (1953), both of which outlined the principles of operant conditioning and their application to human behavior.
Edward Thorndike: Thorndike was one of the first scientists to study the principles of operant conditioning. He developed the “law of effect,” which states that behaviors followed by positive consequences are more likely to be repeated, while behaviors followed by negative consequences are less likely to be repeated. Some of his classic studies include “Animal Intelligence” (1911) and “The Fundamentals of Learning” (1932).
Ivan Pavlov: Pavlov is perhaps best known for his work on classical conditioning, but he also made important contributions to the study of operant conditioning. He studied the effects of rewards and punishments on dogs’ behaviors, and his work provided insights into the role of conditioning in the formation of habits. Some of his classic studies include “The Work of the Digestive Glands” (1897) and “Lectures on Conditioned Reflexes” (1928).
John Watson: Watson was a psychologist who made important contributions to the study of behaviorism, which emphasizes the role of environmental factors in shaping behavior. He studied the principles of operant conditioning and their application to human behavior, and he is perhaps best known for his controversial “Little Albert” study, in which he conditioned a young child to fear a white rat.
These scientists and their classic studies have contributed significantly to our understanding of operant conditioning and its application to human and animal behavior. By studying their work, students can gain a deeper appreciation for the principles of operant conditioning and how they shape the behaviors we exhibit in our daily lives.
Prompt: What principles of learning did Skinner discover and how did he make these discoveries?
B.F. Skinner was a psychologist who made significant contributions to the field of behaviorism and our understanding of operant conditioning. He discovered several principles of learning, which he outlined in his influential work “The Behavior of Organisms” (1938) and later in “Science and Human Behavior” (1953). Here are some of the principles of learning that Skinner discovered:
Reinforcement: Skinner discovered that behaviors that are followed by positive consequences, such as rewards or praise, are more likely to be repeated, while behaviors that are followed by negative consequences, such as punishment or criticism, are less likely to be repeated. He called this principle reinforcement and believed it was a fundamental principle of learning.
Shaping: Skinner also discovered that behaviors can be shaped through reinforcement of successive approximations. This means that a behavior can be broken down into smaller components, and each component can be reinforced until the desired behavior is achieved. This principle is known as shaping, and it is often used in animal training and education.
Extinction: Skinner also discovered that behaviors that are no longer reinforced will eventually become extinct. This means that if a behavior is no longer followed by a positive consequence, the behavior will gradually decrease in frequency until it ceases altogether.
Schedules of reinforcement: Skinner discovered that the frequency and timing of reinforcement can have a significant impact on behavior. He studied different schedules of reinforcement, such as fixed-ratio, variable-ratio, fixed-interval, and variable-interval, and found that each schedule had unique effects on behavior.
Skinner made these discoveries through a combination of laboratory experiments, observations of animal and human behavior, and theoretical analysis. He developed the “Skinner box,” a laboratory apparatus used to study operant conditioning in animals, and he used this device to study the effects of reinforcement and punishment on behavior. He also conducted experiments with humans, such as studying the effects of different reinforcement schedules on study habits. Overall, Skinner’s work had a profound impact on our understanding of learning and behavior, and his discoveries continue to be influential in psychology and related fields.
This is an example of a prompt I used to see how close ChatGPT would get to generating a final segment of text on shaping.
Prompt: Provide an example of how shaping works
Shaping is a process of reinforcing successive approximations of a desired behavior until the final behavior is achieved. This process is often used in animal training, education, and therapy.
Here’s an example of how shaping works in animal training:
Let’s say you want to train a dog to spin in a circle on command. However, the dog has no prior experience with this behavior, so you need to shape the behavior from scratch.
Start by reinforcing any behavior that is even remotely close to spinning in a circle. For example, if the dog turns its head to the side, give it a treat or praise. This will help the dog understand that it is being rewarded for moving in the right direction
Once the dog starts turning its head to the side consistently, raise the criteria by only rewarding the dog when it turns its head a little further. For example, if the dog turns its head 45 degrees to the side, reward it. If it only turns its head 30 degrees, do not reward it.
Keep raising the criteria gradually until the dog is turning in a full circle. This might take several training sessions, as the dog needs to understand what is expected of it at each step of the process.
Finally, add a verbal or visual cue to the behavior, such as saying “spin” or using a hand signal. Once the dog associates this cue with the behavior, it should be able to spin on command.
Through the process of shaping, the dog has learned to perform a complex behavior that it had no prior experience with. This process can be used to teach a wide variety of behaviors, from simple tricks to complex tasks. By breaking down a behavior into small, achievable steps and reinforcing each step along the way, shaping can help animals (and humans) learn and master new skills.
The group of prompts are examples of what I would use to generate material on modeling.
Prompt: What is important for introductory students to know about observational learning
Observational learning, also known as social learning or modeling, is the process of acquiring new behaviors or modifying existing behaviors by observing others. Here are some important points to consider when introducing students to this topic:
The basic principles:
Observational learning involves several key principles, including attention, retention, reproduction, and motivation. These principles suggest that people learn by paying attention to the behaviors of others, retaining information about these behaviors in memory, reproducing the observed behaviors, and being motivated to imitate the behaviors based on their outcomes.
Examples of observational learning:
There are many examples of observational learning, such as learning to cook a new recipe by watching a cooking show, learning to drive a car by observing others, or learning to solve a math problem by watching a teacher demonstrate the steps. These examples illustrate how observational learning can occur in a variety of contexts and for a range of behaviors.
Observational learning and modeling:
Observational learning is sometimes referred to as modeling because it involves the observation of models, or individuals who demonstrate the behavior being learned. Models can be live or symbolic (such as characters on TV or in books), and they can influence behavior through both positive and negative reinforcement.
Observational learning and cognitive processes:
Observational learning involves more than just watching others; it also involves cognitive processes such as attention, memory, and motivation. Observational learning can be influenced by factors such as the salience of the behavior being observed, the similarity between the observer and the model, and the perceived outcome of the behavior.
Applications of observational learning:
Observational learning has many practical applications, such as in education, parenting, and advertising. By understanding the principles of observational learning, individuals can use modeling to promote positive behaviors and discourage negative ones.
Overall, observational learning is a complex process that involves cognitive and behavioral factors. By understanding the principles of observational learning, students can gain insights into how they learn and how they can use modeling to promote their own growth and development.
The following prompts expand the basic material on modeling to include a classic study (Bandura’s bobo doll experiment) and then extend this classic experiment to consider how aggressive behavior might be encouraged by playing certain types of video games.
Prompt: Describe the bobo doll experiment
The Bobo doll experiment was a study conducted by psychologist Albert Bandura in 1961 to examine how social learning occurs in children. The experiment involved exposing children to a video of an adult aggressively hitting and verbally abusing an inflatable doll called “Bobo.”
In the experiment, Bandura divided children into three groups. The first group was exposed to the video of the adult hitting and yelling at the doll. The second group was shown a video of an adult playing with the doll in a non-aggressive manner. The third group was not shown any videos.
After watching the videos, the children were brought into a room filled with toys, including the Bobo doll. The researchers observed how the children behaved towards the doll. The results of the study showed that children who were exposed to the aggressive video were more likely to exhibit aggressive behavior towards the doll compared to the other two groups. They imitated the behavior they had witnessed in the video by hitting, kicking, and verbally abusing the Bobo doll.
The Bobo doll experiment demonstrated that children learn through observation and imitation. It also highlighted the importance of modeling appropriate behavior for children and being mindful of the types of behaviors that children are exposed to in their environments. The study had a significant impact on the field of psychology and our understanding of social learning theory.
Prompt: What are the implications of this experiment? What real world behaviors might it explain?
The Bobo doll experiment has important implications for our understanding of how children learn and develop behavior. Some of the key implications of the experiment are:
Children learn through observation and imitation: The experiment showed that children can learn new behaviors by watching others. They may imitate positive or negative behaviors, depending on what they observe.
Aggressive behavior can be learned: The experiment demonstrated that exposure to aggressive behavior can lead to children imitating that behavior. This suggests that violent behavior in the media, such as in video games and movies, may contribute to aggressive behavior in children.
Modeling appropriate behavior is important: The study highlighted the importance of modeling appropriate behavior for children. Adults need to be mindful of the behaviors they exhibit in front of children and ensure they model positive behaviors.
Social learning theory has important implications: The study provided support for social learning theory, which suggests that people learn from observing others in their environment.
In terms of real-world behaviors, the Bobo doll experiment may explain why some children are more likely to engage in aggressive behavior, especially if they have been exposed to violence in their environment. It also highlights the potential negative impact of violent media on children. Additionally, the experiment suggests that positive role models, such as parents and teachers, can have a significant impact on a child’s behavior by modeling appropriate behaviors
Prompt: Cite research that shows some video games increase aggression
There is a significant body of research that suggests that some video games can increase
There is a significant body of research that suggests that some video games can increase aggression in children and adolescents. Here are a few examples:
Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78(4), 772-790. This study found that playing violent video games led to increased aggression in participants, both in the laboratory and in real-life situations.
Greitemeyer, T., & Mügge, D. O. (2014). Video games do affect social outcomes: A meta-analytic review of the effects of violent and prosocial video game play. Personality and Social Psychology Bulletin, 40(5), 578-589. This meta-analysis of multiple studies found that playing violent video games was associated with increased aggression and decreased prosocial behavior
Ferguson, C. J., & Kilburn, J. (2010). The public health risks of media violence: A meta-analytic review. Journal of Pediatrics, 154(5), 759-763. This meta-analysis found that exposure to violent video games was associated with increased aggression, as well as other negative outcomes such as decreased empathy and increased desensitization to violence.
Gentile, D. A., Lynch, P. J., Linder, J. R., & Walsh, D. A. (2004). The effects of violent video game habits on adolescent hostility, aggressive behaviors, and school performance. Journal of Adolescence, 27(1), 5-22. This study found that playing violent video games was associated with increased aggression and decreased academic performance in adolescents.
These are just a few examples of the research suggesting that some video games can increase aggression. While the relationship between video games and aggression is complex and not fully understood, these studies provide evidence that parents and caregivers should be mindful of the types of games their children are playing and limit exposure to violent content if necessary.
Prompt: Describe the Anderson and Dill study
The Anderson and Dill study (2000) investigated the relationship between playing violent video games and aggressive thoughts, feelings, and behaviors. The study was conducted in two parts: a laboratory experiment and a field study.
For the laboratory experiment, the researchers randomly assigned 210 college students to play either a violent or non-violent video game for 20 minutes. The violent game was the first-person shooter game “Mortal Kombat,” while the non-violent game was the sports game “Tetris.” After playing the game, the participants completed a series of tasks designed to measure aggressive thoughts, feelings, and behaviors.
The results showed that participants who played the violent game had more aggressive thoughts and feelings compared to those who played the non-violent game. They also had higher levels of physiological arousal, as measured by heart rate and skin conductance. Additionally, when given the opportunity to aggress against another person in a separate task, those who played the violent game were more likely to administer louder and longer-lasting noise blasts to their opponent.
For the field study, the researchers surveyed 227 high school students about their video game habits and levels of aggression. They found that students who played more violent video games were more likely to engage in physical fights, verbal arguments, and other forms of aggressive behavior.
Overall, the Anderson and Dill study provided evidence that playing violent video games can increase aggressive thoughts, feelings, and behaviors. While the study was conducted with college and high school students, the findings suggest that parents and caregivers should be mindful of the types of games their children are playing and limit exposure to violent content if necessary.
Summary:
This post was intended as a proof of concept explaining how I could make use of ChatGPT and the knowledge already in my head from multiple years of teaching the Course Introduction to Psychology to produce a reasonable textbook for this course.
The approach I have outlined here made use of ChatGPT to generate an outline of topics and subtopics appropriate to one chapter of this textbook. ChatGPT was then asked to produce explanations of items from the outline, produce examples and descriptions of research appropriate to these concepts, and possible applications of the core ideas from these concepts.
My proposal is that an experienced individual in combination with ChatGPT could rapidly generate the content for a survey course. I am not proposing that this product would be a great product, but it would be the reasonable equivalent of commercial products generated at no cost beyond my time. I had those interested in OER content in mind in exploring this option.
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