AI as Extraherics: Fostering Higher-order Thinking Skills in Human-AI Interaction (2409.09218v2)

Abstract: As AI technologies, including generative AI, continue to evolve, concerns have arisen about over-reliance on AI, which may lead to human deskilling and diminished cognitive engagement. Over-reliance on AI can also lead users to accept information given by AI without performing critical examinations, causing negative consequences, such as misleading users with hallucinated contents. This paper introduces extraheric AI, a human-AI interaction conceptual framework that fosters users' higher-order thinking skills, such as creativity, critical thinking, and problem-solving, during task completion. Unlike existing human-AI interaction designs, which replace or augment human cognition, extraheric AI fosters cognitive engagement by posing questions or providing alternative perspectives to users, rather than direct answers. We discuss interaction strategies, evaluation methods aligned with cognitive load theory and Bloom's taxonomy, and future research directions to ensure that human cognitive skills remain a crucial element in AI-integrated environments, promoting a balanced partnership between humans and AI.

• The paper introduces extraheric AI, an approach that enhances higher-order thinking by encouraging users to analyze, synthesize, and evaluate information.
• It outlines eight interaction strategies, such as questioning and nudging, that actively engage users in deep cognitive processes.
• Evaluation methods blend cognitive theory with educational frameworks to measure cognitive load and attitudinal shifts, underscoring practical design implications.

An Analysis of "AI as Extraherics: Fostering Higher-order Thinking Skills in Human-AI Interaction"

The paper "AI as Extraherics: Fostering Higher-order Thinking Skills in Human-AI Interaction" by Yatani, Sramek, and Yang presents a novel approach in human-AI interaction called extraheric AI. The core premise revolves around AI systems that, instead of merely replacing or augmenting human cognition, are designed to enhance higher-order cognitive skills, including creativity, critical thinking, and problem-solving. This paper contends that the prevalent interaction designs which aim to support and streamline user tasks can inadvertently lead to human deskilling and reduced cognitive engagement.

Central Thesis and Conceptual Framework

The authors introduce extraheric AI as an interaction paradigm tasked with fostering cognitive engagement by encouraging users to analyze, synthesize, and evaluate information rather than provide straightforward solutions. This contrasts with conventional AI designs described as orthotics, prosthetics, and exoskeletons, which predominantly aim to enhance or replace cognitive tasks. Extraheric AI, derived from the Latin 'extraho' meaning 'to draw forth', intends to draw out users' latent cognitive abilities during task pursuit, promoting active cognitive processing and learning through engagement rather than passivity.

Interaction Strategies

The paper filtered existing AI implementations and identified eight interaction strategies relevant to extraheric AI:

1. Suggesting Content: Recommends diverse ideas and approaches for users to evaluate.
2. Explaining: Provides background or context to facilitate better comprehension.
3. Nudging: Gently influences user actions by subtly presenting alternative perspectives.
4. Debating Content: Promotes discussion with AI agents offering varied perspectives.
5. Questioning: Encourages users to think critically about their viewpoints.
6. Scaffolding: Suports users in mastering complex tasks via partial assistance.
7. Simulating: Offers simulated experiences to broaden understanding.
8. Demonstrating: Enables learning through vicarious observation of AI actions.

These strategies innovate beyond task efficiency, aiming instead to sustain and improve users' cognitive capabilities.

Evaluation and Implications

For evaluation, the authors suggest blending cognitive theory and educational frameworks like Bloom's Taxonomy and Sweller's cognitive load theory. The focus is on capturing the cognitive load—particularly germane load, which aligns with cognitive engagement—and assessing changes in higher-order thinking skills broadly categorized into knowledge, comprehension, application, analysis, and synthesis. Evaluating attitudinal changes related to agency, self-efficacy, motivation, and responsibility attribution forms a significant part of the proposed evaluation framework.

The implications of adopting an extraheric approach are broad and multifaceted. From a practical standpoint, such AI systems call for adaptive designs integrating into users' workflows without compromising task efficacy or overwhelming them. Theoretically, it demands reevaluation of existing human-AI interaction paradigms and further research into diversified AI outputs to stimulate higher cognitive processing. Additionally, understanding the social roles of AI and maintaining ethical considerations are critical areas necessitating in-depth exploration.

Future Directions

The paper encourages further exploration into AI technologies that effectively present diverse perspectives without biasing user judgment. It also points to developing social learning environments where multiple AI agents simulate varied perspectives, enhancing social learning dynamics typically seen in human-to-human interaction.

By marrying cognitive theory with practical AI design, "AI as Extraherics" lays the groundwork for a refined AI interaction paradigm, emphasizing balanced cognitive partnerships between humans and intelligent systems. As AI continues to penetrate diverse domains, embracing such frameworks could redefine its role from mere tool towards becoming an enabler of deep-seated cognitive growth.