Tuning AI to Work Heuristically in an Individual’s Zone of Proximal Development for Literary Reading Pedagogy
AI Does NOT Mean Artificial Imagination
Agree or disagree or ignore this premise: AI can read and comprehend literary text.
Agree or disagree or ignore this corollary: AI can read and comprehend poetry.
Agree or disagree with this response: AI gives me a headache.
“Over the past 50 years, the notion of ‘heuristics’ has considerably gained attention in fields as diverse as psychology, cognitive science, decision theory, computer science, and management scholarship. While for 1970, the Scopus database finds a meagre 20 publisted articles with the word ‘heuristic’ in their title, the number has increased to no less than 3783 in 2021 (Scopus, 2022)” (Hjeij and Vilks, 2023).
SoR ©️ is beginning to reveal to me ever more clearly its deep roots in a once useful heuristic theory during Simpler Times, rather than a scientific or even philosophical theory. As others more attuned to the historical nuances of theoretical models of reading than I have commented, the Simple View of Reading is way way way oversimplified, a handy way of thinking about a perplexing and persistent pedagogical problem growing less and less useful. Although it uncovers undeniably valid inferences about what must be happening when we read, its obviousness is its weakness. Reading is an invisible process.
As the recent shift toward inclusion of “knowledge building” and “writing” under the auspices of SoR ©️ intensifies, the thinning out of nuances and the obfuscation of theoretical failures drive narrow, algorithmic views of pedagogy toward summary of a text as comprehension of a text and writing a summary as writing. As pedagogical heuristics are prone to do, an impoverished and imperial perspective on autonomous literacy drives a wedge between teaching and learning.
Of course, if reading means comprehension, if comprehension means summarizing, if retelling means writing, the language bot is a grave danger to learning. Some might think it does these things better than most kids, though I don’t think kids would read in so constrained and safe ways without pressure. If measurement of comprehension is a series of correct answers to multiple choice questions or machine-scored summaries, students do their reading that counts under surveillance and the possibility of penalties for accessing technology.
Texas Instruments (TI) and Hewlett-Packard (HP) revolutionized the market for calculators in the 1970s. Texas Instruments introduced the first handheld calculator in 1967, the "Cal-Tech," and followed up with the TI-2500 Datamath in 1972. During this year, my first year in college, Hewlett-Packard launched the HP-35, the first handheld scientific calculator. These innovations paved the way for widespread calculator adoption in classrooms.
Many teachers were gripped by a fear. They imagined a dystopian classroom of gadgets replacing mental arithmetic and pencil-and-paper problem-solving. Students would become button-pushing, unthinking automatons. Today, those visionary early adopters can look back and smile in retirement. It’s like fearing the printing press would end handwriting. But what happened? Oh, the Renaissance, the Enlightenment. Calculators made complex concepts accessible and freed up valuable classroom time for exploring the magic and feeling the joy of mathematics.
I had a long chat with the bot about how AI is being greeted with a similar reception in literacy education. After the chat, I commanded it to write an essay about a fictitious digital product called the Underassistant Literary bot. Here’s the prompt: “Write an informative essay aimed at educating high school history and English teachers who have a professional interest in teaching literary reading and personal response. Highlight the value of the new Underassistant Literary bot as a teaching tool with the capacity to provide the most minimal help to keep readers productively moving through complex literature, including poetry and drama.”
Here’s the essay, the bot’s first draft, no editing other than deleting some hashtags, fresh out of the oven:
Revolutionary Teaching with Underassistant Literary Bot
Teaching literature, especially complex texts, is a challenging yet rewarding task for high school history and English teachers. The goal is to foster critical thinking, comprehension, and a personal connection to the texts among students. The Underassistant Literary bot is a cutting-edge digital tool that can significantly aid educators in achieving these objectives.
Enhancing Comprehension
One of the greatest challenges students face with complex literature, including poetry and drama, is comprehension. The Underassistant Literary bot can provide definitions, historical contexts, and brief explanations of difficult passages. This minimal assistance can prevent students from becoming discouraged by dense texts, thus maintaining their engagement and motivation. By only providing essential insights, the bot encourages students to grapple with the material themselves, promoting deeper understanding and retention.
Promoting Analytical Skills
The Underassistant Literary bot is designed to prompt students to think critically about what they read. It can ask probing questions about the text, encourage students to make connections to broader themes, and suggest potential areas of literary analysis. This kind of guided inquiry helps students develop their analytical skills without spoon-feeding them interpretations. The aim is to stimulate independent thought, prompting students to come to their own conclusions about the literature.
Facilitating Personal Response
Literature is most powerful when it resonates personally with the reader. The Underassistant Literary bot can help students articulate their personal responses to the text. It might prompt students to reflect on how the themes relate to their own experiences, or to consider how they feel about the characters' decisions. By facilitating these personal connections, the bot helps students find relevance in the literature, fostering a lifelong appreciation for reading.
Supporting Teachers
For educators, the Underassistant Literary bot serves as an invaluable support tool. It can handle more routine questions from students, freeing up teachers to focus on more complex discussions and personalized instruction. Teachers can also use the bot to supplement their lesson plans, ensuring that every student receives the help they need, even in larger classes.
Encouraging Collaboration
The bot also promotes collaborative learning. It can be used in group activities, where students can discuss their responses to the bot's prompts. This encourages peer-to-peer learning and helps students develop their communication skills. Collaborative analysis of literature can lead to richer, more varied interpretations and a more inclusive classroom discussion.
Innovating with Technology
Incorporating the Underassistant Literary bot into the classroom also prepares students for the future. It teaches them how to effectively use digital tools as part of their learning process, a skill that is increasingly important in all fields. By blending traditional literary analysis with modern technology, educators can provide a more dynamic and engaging learning experience.
In conclusion, the Underassistant Literary bot represents a significant advancement in educational technology, offering teachers a powerful tool to enhance the teaching and learning of complex literature. By providing minimal yet essential assistance, it helps students stay engaged, think critically, and form personal connections with the text, all while supporting educators in delivering more effective and meaningful instruction.
*****
Here I’m providing relevant portions of the chat leading up to this essay. Skip any or all of it as you wish. My first command comes from the excerpt quoted at the beginning of this post, specifically building from this excerpted sentence: “Over the past 50 years, the notion of ‘heuristics’ has considerably gained attention in fields as diverse as psychology, cognitive science, decision theory, computer science, and management scholarship.”
ME: What do these academic fields have in common? How is it that the term heuristic has intensified as a research topic in these areas over the past fifty years?
psychology, cognitive science, decision theory, computer science, and management scholarship
GPT4.0: The academic fields of psychology, cognitive science, decision theory, computer science, and management scholarship share an interest in understanding and improving human and machine decision-making processes. Over the past fifty years, the term "heuristic" has intensified as a research topic in these areas due to several interrelated reasons:
### Common Interest in Decision-Making
1. **Psychology**: Studies how humans think, perceive, and make decisions. Heuristics are simple, efficient rules people often use to form judgments and make decisions.
2. **Cognitive Science**: Examines the nature of human thought and intelligence. Heuristics are fundamental due to their role in simplifying complex cognitive processes.
3. **Decision Theory**: Analyzes how decisions are made, the processes involved, and how they can be improved. Heuristics are crucial for understanding human biases and optimizing decision strategies.
4. **Computer Science**: Focuses on algorithms and computational models for problem-solving. Heuristics are essential in developing algorithms that can solve problems efficiently, especially when exact solutions are computationally infeasible.
5. **Management Scholarship**: Studies organizational behavior, strategy, and decision-making. Heuristics help in developing practical decision-making tools and strategies for managers.
### Evolution of Heuristics as a Research Topic
1. **Shift from Rational Models**: Traditional models assumed entirely rational decision-making processes, but real-world scenarios showed deviations due to cognitive limitations. This highlighted the importance of heuristics.
2. **Behavioral Economics**: The work of researchers like Daniel Kahneman and Amos Tversky brought heuristics to prominence by demonstrating how they can lead to systematic biases and errors (e.g., prospect theory).
3. **Computational Advances**: In computer science and artificial intelligence, the development of heuristics has improved the efficiency of algorithms, making them practical for solving real-world problems like navigation (e.g., A* algorithm).
4. **Interdisciplinary Research**: Collaboration across these fields has enriched the understanding of heuristics. Cognitive models in psychology have informed algorithms in computer science, while decision theory has provided frameworks for management research.
5. **Applied Relevance**: In management, simple heuristics have been promoted as valuable tools for decision-making in complex and uncertain environments. This is echoed in cognitive science, which recognizes the adaptability and robustness of heuristic strategies.
Heuristics have thus become a focal point because they offer a bridge between idealized decision models and the practical realities of human and machine problem-solving, providing simple yet effective methods to navigate complex and imperfect information landscapes.
ME: Give me five examples of the following:
*Psychology**: Studies how humans think, perceive, and make decisions. Heuristics are simple, efficient rules people often use to form judgments and make decisions
GPT4: Sure, here are five examples related to the study of heuristics in psychology:
1. **Availability Heuristic**:
- People estimate the likelihood of events based on how easily examples come to mind. For instance, after seeing news reports about airplane accidents, individuals might overestimate the dangers of air travel.
2. **Representativeness Heuristic**:
- People judge the probability of an event based on how much it resembles their existing stereotypes. For instance, assuming a quiet, studious person is more likely to be a librarian than a salesperson because they fit the stereotype of a librarian.
3. **Anchoring Heuristic**:
- People rely too heavily on the first piece of information (the "anchor") when making decisions. For example, if initially asked if the population of a city is more than 1 million and then to guess the actual number, their guess tends to be closer to the 1 million mark.
4. **Gambler's Fallacy**:
- Believing that past events influence the likelihood of future events in a random sequence. For example, thinking that after several coin flips resulting in heads, the next flip is more likely to be tails.
5. **Affect Heuristic**:
- Decisions are influenced by emotions and feelings. For instance, if someone feels good about a person or situation, they might perceive the risks as lower and the benefits as higher than they objectively are.
These heuristics demonstrate how cognitive shortcuts can influence our judgments and decision-making processes.
ME: Are there positive examples?
GPT4: Absolutely, heuristics can simplify complex decision-making and can lead to effective and timely decisions. Here are five positive examples of how heuristics aid decision-making:
1. **Recognition Heuristic**:
- When faced with multiple choices, people might choose the one they recognize. For example, while choosing between two brands of cereal, opting for the brand you've heard of before can speed up the decision-making process, ensuring you select a reliable option.
2. **Frequency Heuristic**:
- Judging the frequency of an event by the number of instances easily recalled. For instance, a doctor might quickly diagnose a common illness because they frequently encounter similar cases, enabling faster and accurate treatment.
3. **Social Proof Heuristic**:
- People follow the behavior of others to make decisions, particularly in uncertain situations. For example, choosing a restaurant that's busy because it indicates it's probably good, thereby efficiently identifying quality options.
4. **Default Heuristic**:
- Sticking with pre-set options can lead to good outcomes. For example, many people stick with the default settings on devices, which are typically configured to provide a balance of performance and convenience based on broad user studies.
5. **Tallying Heuristic**:
- Simplifying decision-making by summing cues and using the most cues to determine the best option. For instance, when buying a used car, considering factors like mileage, condition, make, and year, the car with the most positive attributes tallied can streamline and improve the purchasing decision.
These heuristics highlight how simplified rules can lead to effective and satisfactory choices when navigating complex environments.
I had an artificially wonderful chat with the bot about specific heuristics students apply during reading comprehension which I’ll skip. I will say this: The bot is a relatively unsophisticated purveyor of common sense. It has all of the nose-to-the-grindstone grin-and-bear-it platitudes you hear everywhere—from skimming to boldfaced words. I’m sure if this were a specially trained bot on the whole corpus of the Handbook of Research in Reading which in all its volumes may rival the Oxford English Dictionary I would be excited by the results with more than a handful of scholars I would need to run through the campus library databases.
Here’s the next relevant section:
ME: Are there any, more sophisticated heuristics readers apply during the reading of complex serious literature?
GPT4: Absolutely! When reading complex and serious literature, readers often engage in more sophisticated heuristic strategies to navigate and interpret dense material. Here are a few examples:
1. **Metaphor and Symbol Interpretation:**
- **Example:** In Kafka’s "The Metamorphosis," Gregor Samsa’s transformation into a giant insect can be interpreted as a symbol of alienation and loss of humanity.
- **Heuristic:** Recognizing and analyzing symbols and metaphors to uncover deeper meanings beyond the literal text.
2. **Intertextual References:**
- **Example:** Understanding T.S. Eliot's "The Waste Land" requires familiarity with numerous literary, religious, and historical texts.
- **Heuristic:** Identifying references to other texts and incorporating that knowledge to enhance comprehension and interpretation.
3. **Narrative Structure Analysis:**
- **Example:** In James Joyce’s "Ulysses," the fragmented and stream-of-consciousness narrative mirrors the complexity of human thought and consciousness.
- **Heuristic:** Analyzing the narrative structure to understand how it reflects themes and character psychology.
4. **Tone and Mood Dissection:**
- **Example:** Interpreting the bleak tone in George Orwell’s "1984" as a critique of totalitarianism and its dehumanizing effects.
- **Heuristic:** Distinguishing the nuances of tone and mood to uncover the author’s underlying messages and themes.
5. **Character Motivations and Development:**
- **Example:** Analyzing Raskolnikov’s internal conflict in Dostoevsky’s "Crime and Punishment" to understand his psychological and moral struggles.
- **Heuristic:** Delving into character development and motivations to gain insight into their actions and the narrative’s broader implications.
6. **Historical and Cultural Contextualization:**
- **Example:** Understanding "One Hundred Years of Solitude" by Gabriel Garcia Marquez requires knowledge of Latin American history and magical realism.
- **Heuristic:** Placing the text within its historical and cultural context to better grasp the significance of themes and events.
7. **Theme Integration:**
- **Example:** Recognizing the recurring theme of existentialism in Albert Camus' "The Stranger" to understand the protagonist’s detachment from society.
- **Heuristic:** Identifying and integrating major themes throughout the text to comprehend its overall message and purpose.
8. **Philosophical and Ethical Inquiry:**
- **Example:** Engaging with the ethical dilemmas presented in Fyodor Dostoevsky’s "The Brothers Karamazov."
- **Heuristic:** Applying philosophical and ethical frameworks to evaluate the moral quandaries and ideological debates within the text.
These heuristics involve deeper cognitive engagement, making connections beyond the text, and applying critical thinking to unveil the complex layers of meaning in serious literature.
ME: Which of these more sophisticated heuristics would be good starting points for teachers to introduce students to the art of reading literature with AI assistance?
GPT4: Introducing students to the art of reading literature with AI assistance can be fascinating and beneficial. Here are some sophisticated heuristics that can be good starting points:
1. **Sentiment Analysis**: AI can analyze the emotional tone of a text, helping students understand the mood and emotional landscape of a piece of literature.
2. **Thematic Analysis**: AI can identify recurring themes and motifs within a text, providing students with insights into the deeper meaning and underlying messages.
3. **Character Analysis**: AI can track character development, relationships, and traits throughout a story, aiding students in understanding character arcs and dynamics.
4. **Stylistic Analysis**: AI can examine the author’s writing style, including syntax, diction, and rhetorical devices, helping students appreciate the nuances of literary craft.
5. **Plot Structure Analysis**: AI can outline the plot structure, identifying key events such as the exposition, rising action, climax, and resolution. This helps students grasp the narrative flow.
6. **Intertextual Analysis**: AI can draw comparisons between texts, highlighting allusions, references, and influences. This allows students to see connections and influences across different works.
7. **Historical and Cultural Context**: AI can provide background information on the historical and cultural context of a literary work, giving students a richer understanding of its significance.
Starting with one or more of these heuristics will help students develop a comprehensive approach to reading and analyzing literature with the aid of AI.
ME: But these suggestions seem to take over the work of the reader. How would you know when to offer help without barging in on the reader?
GPT4: Balancing AI assistance with the reader’s active engagement is crucial. Here are some strategies to ensure AI aids rather than overtakes the reader’s work:
1. **Interactive Queries**: Allow students to ask specific questions or request assistance when they feel stuck or curious about a particular aspect of the text. This respects their reading process and offers help as needed.
2. **Highlight Key Moments**: Instead of providing a full analysis, the AI can highlight sentences or paragraphs that are significant and suggest students analyze them. This keeps the primary engagement with the students.
3. **Provide Hints, Not Answers**: Offer subtle hints or prompts to guide students toward deeper understanding rather than outright answers. For example, “Consider how the setting influences the character’s mood in this chapter.”
4. **Choice-Based Assistance**: Let students choose which type of analysis they want at any given time. For instance, they might request a thematic analysis for one chapter and a stylistic analysis for another.
5. **Incremental Assistance**: Start with minimal help and gradually offer more assistance based on students’ interactions and feedback. If they seem to grapple with certain concepts, the AI can provide more in-depth help.
6. **Encourage Critical Thinking**: Pose open-ended questions that encourage students to think critically about the text before revealing AI insights. For instance, “What do you think the author is trying to convey with this recurring symbol?”
7. **Collaborative Analysis**: Treat the AI as a companion in the analysis. For instance, after identifying a theme, the AI could ask, “What do you think this theme suggests about the characters’ motivations?”
In these ways, the AI can enhance the reading experience by supporting students’ learning processes without overshadowing their role as active readers and interpreters of literature.
ME: Would a bot need special training to underassist readers we will call it in the ways you suggest?
GPT4: Yes, a bot would indeed require special training to "underassist" readers as described. Here are a few considerations and steps that would make this possible:
### 1. **Natural Language Processing (NLP) Training:**
- **Context Awareness:** Train the bot to understand contextual cues within text. This allows it to offer hints rather than direct answers, facilitating gradual assistance.
- **Sentiment Analysis:** Implement sentiment analysis to gauge when a student is frustrated or confused, adjusting the assistance level accordingly.
### 2. **User Interaction Data:**
- **Feedback Loops:** Collect data on how users interact with the bot to continuously refine its assistance levels.
- **Personalization:** Use machine learning to personalize the bot’s responses based on individual user behavior and preferences.
### 3. **Educational Pedagogy Integration:**
- **Scaffolded Learning:** Implement principles of scaffolded learning, where the bot is trained to provide progressively less support as a user’s understanding improves.
- **Critical Thinking Prompts:** Embed pedagogical strategies that encourage critical thinking through open-ended questions and prompts.
### 4. **Guidelines and Rules:**
- **Assistance Tiers:** Create guidelines for varying levels of assistance—from basic hints to more detailed explanations—triggered by user needs.
- **Restrained Responses:** Develop rules that prioritize hints and questions over direct answers, ensuring the bot nudges users toward discovering answers themselves.
### 5. **User Choice Mechanisms:**
- **Options for Help:** Implement choice-driven assistance, where users can select the type and level of help they need.
- **Customizable Settings:** Allow users to adjust the bot’s assistance level, creating a personalized learning experience.
### 6. **Ethical Considerations:**
- **Transparency:** Ensure the bot is transparent about its role and limitations, explaining why it might be withholding direct answers to encourage learning.
- **User Consent:** Provide options for users to consent to different levels of assistance, ensuring comfort and control over their learning experience.
### 7. **Iterative Improvement:**
- **Feedback Integration:** Regularly update the bot’s training data based on user feedback to refine its ability to offer just the right amount of help.
- **Pilot Testing:** Conduct pilot programs to test the bot’s effectiveness in educational settings, adjusting strategies based on empirical results.
Developing a bot capable of nuanced, "underassisting" behavior involves a blend of advanced technological capabilities and a deep understanding of educational pedagogies. By doing so, the bot can enhance learning without overshadowing the active engagement and critical thinking that are vital to the reader’s experience.
ME: How could curriculum administrators at a school or district acquire or have trained an underassisting literary response bot?
GPT4: To acquire or train an underassisting literary response bot for a school or district, curriculum administrators can follow these steps:
### Acquisition
1. **Identify Needs:**
- Determine the specific functionalities the bot should have (e.g., analyzing texts, generating responses, helping with essay writing).
2. **Research Options:**
- Look for existing educational AI bots that specialize in literary responses. Popular options include:
- Google’s Dialogflow
- IBM Watson Education
- Custom-built solutions from tech companies
3. **Evaluate Tools:**
- Compare features, ease of integration, scalability, and cost.
- Check user reviews and case studies from other educational institutions.
4. **Vendor Engagement:**
- Contact the chosen vendor for demonstrations and pilot programs.
- Negotiate terms and finalize the acquisition.
### Training
1. **Gather Data:**
- Collect relevant literary materials, including texts, sample responses, and grading rubrics.
- Ensure data diversity to better train the bot for various types of literary analysis and response.
2. **Curriculum Developer Collaboration:**
- Collaborate with curriculum developers to design the bot’s training program.
- Define response criteria and quality standards.
3. **Input Data:**
- Feed the bot with collected literary data and example responses.
- Use annotated texts to help the bot understand different literary elements (e.g., themes, symbols, metaphors).
4. **Supervised Learning:**
- Implement supervised learning where educators review the bot’s responses and provide feedback.
- Continuously refine the bot’s algorithms based on feedback.
5. **Testing:**
- Conduct extensive testing with sample student assignments.
- Identify weaknesses and continue calibrating the bot until it meets the desired accuracy and usefulness standards.
### Implementation
1. **Integration:**
- Integrate the bot with the school or district’s existing digital platforms (e.g., LMS, email systems).
2. **Pilot Program:**
- Launch a pilot program in select classrooms to monitor performance and gather feedback.
3. **Training for Teachers:**
- Provide training sessions for teachers on how to use the bot effectively.
- Create user manuals and resources.
4. **Monitoring and Iteration:**
- Constantly monitor the bot’s performance in real-world scenarios.
- Gather feedback from students and teachers to identify areas for improvement.
5. **Scaling Up:**
- Once the bot performs reliably, roll it out to more classrooms or district-wide.
- Continue providing support and updates to ensure it remains effective.
By following these steps, curriculum administrators can successfully acquire and train a literary response bot that enhances teaching and learning experiences.
ME: Write an informative essay aimed at educating at high school history and English teachers who have a professional interest in teaching literary reading and personal response. Highlight the value of the new Underassistant Literary bot as a teaching tool with the capacity to provide the most minimal help to keep readers productively moving through complex literature, including poetry and drama.