Humans often rely on underlying structural patterns-schemas-to create, whether by writing stories, designing software, or composing music. Schemas help organize ideas and guide exploration, but they are often difficult to discover and apply, especially in complex or unfamiliar domains. My Ph.D. research develops a framework for human-AI schema discovery and application to support creative problem solving. I design systems that support users in sensemaking over examples to abstract schemas, and in operationalizing schemas into human-AI co-creative workflows for application. This research offers insights into how schema-guided interaction can make implicit knowledge more accessible and actionable, advancing more transparent and collaborative human-AI systems.

The recent leaps in complexity and fluency of Large Language Models (LLMs) mean that, for the first time in human history, people can interact with computers using natural language alone. This creates monumental possibilities of automation and accessibility of computing, but also raises severe security and safety threats: When everyone can interact with LLMs, everyone can potentially break into the systems running LLMs. All it takes is creative use of language. This paper presents Hacc-Man, a game which challenges its players to "jailbreak" an LLM: subvert the LLM to output something that it is not intended to. Jailbreaking is at the intersection between creative problem solving and LLM security. The purpose of the game is threefold: 1. To heighten awareness of the risks of deploying fragile LLMs in everyday systems, 2. To heighten people's self-efficacy in interacting with LLMs, and 3. To discover the creative problem solving strategies, people deploy in this novel context.

In Given this overview, we see that LLVMs both at the highlevel this section, we discuss how typical task planning is achieved and low-level, can be modified to incorporate creative with LLVMs. We divide the discussion into three subsections problem solving into task planning. For instance, the high-level based on the level of task planning abstraction where LLVMs task plans generated can encompass a novel substitution for a are applied: a) high-level task planning, b) low-level task missing object, whereas the low-level task plan can generate planning, and c) hybrid task planning.

Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence (AI) that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems. Despite many advancements in planning and learning, resolving novel problems or adapting existing knowledge to a new context, especially in cases where the environment may change in unpredictable ways post deployment, remains a limiting factor in the safe and useful integration of intelligent systems. The emergence of increasingly autonomous systems dictates the necessity for AI agents to deal with environmental uncertainty through creativity. To stimulate further research in CPS, we present a definition and a framework of CPS, which we adopt to categorize existing AI methods in this field. Our framework consists of four main components of a CPS problem, namely, 1) problem formulation, 2) knowledge representation, 3) method of knowledge manipulation, and 4) method of evaluation. We conclude our survey with open research questions, and suggested directions for the future.

One pioneering group at the Massachusetts Institute of Technology (MIT) is applying emotion AI to improve mental health and overall quality of life. Recently the Affective Computing Research Group at the MIT Media Lab published a new study that provides empirical evidence that empathetic artificial intelligence (AI) machine learning can counterbalance the adverse effects of anger on human creative problem solving. The MIT study involved over a thousand participants to play a word guessing game, Wordle, to see how anger and empathy impacts performance. Those assigned to the anger elicitation condition performed poorly compared to the control group. The research shows that an empathic AI agent can reduce the negative impact of anger on creative problem solving.

The description of "artificial intelligence in high school" may conjure up a science fiction novel where robots stand around chatting at their lockers. The reality, at Seckinger High School in Gwinnett County, Ga., looks more like this: A social studies teacher pauses a lesson on the spread of cholera in the 19th century to discuss how data scientists use AI tools today to track diseases. A math class full of English-language learners uses machine learning to identify linear and non-linear shapes. The simplest explanation of this technology is that it trains a machine to do tasks that simulate some of what the human brain can do. That means it can learn to do things like recognize faces and voices (helpful for radiology, security, and more), understand natural language, and even make recommendations.

How does empathy influence creative problem solving? We introduce a computational empathy intervention based on context-specific affective mimicry and perspective taking by a virtual agent appearing in the form of a well-dressed polar bear. In an online experiment with 1,006 participants randomly assigned to an emotion elicitation intervention (with a control elicitation condition and anger elicitation condition) and a computational empathy intervention (with a control virtual agent and an empathic virtual agent), we examine how anger and empathy influence participants' performance in solving a word game based on Wordle. We find participants who are assigned to the anger elicitation condition perform significantly worse on multiple performance metrics than participants assigned to the control condition. However, we find the empathic virtual agent counteracts the drop in performance induced by the anger condition such that participants assigned to both the empathic virtual agent and the anger condition perform no differently than participants in the control elicitation condition and significantly better than participants assigned to the control virtual agent and the anger elicitation condition. While empathy reduces the negative effects of anger, we do not find evidence that the empathic virtual agent influences performance of participants who are assigned to the control elicitation condition. By introducing a framework for computational empathy interventions and conducting a two-by-two factorial design randomized experiment, we provide rigorous, empirical evidence that computational empathy can counteract the negative effects of anger on creative problem solving.

Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence (AI) that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems. Despite many advancements in planning and learning, resolving novel problems or adapting existing knowledge to a new context, especially in cases where the environment may change in unpredictable ways post deployment, remains a limiting factor in the safe and useful integration of intelligent systems. The emergence of increasingly autonomous systems dictates the necessity for AI agents to deal with environmental uncertainty through creativity. To stimulate further research in CPS, we present a definition and a framework of CPS, which we adopt to categorize existing AI methods in this field. Our framework consists of four main components of a CPS problem, namely, 1) problem formulation, 2) knowledge representation, 3) method of knowledge manipulation, and 4) method of evaluation. We conclude our survey with open research questions, and suggested directions for the future.

We propose a categorization of solution-centric evaluation metrics for a class of domain-independent AI challenge tasks known as MacGyver problems. Our definitions formally describe different classes of novel solutions for general creative problem solving tasks described in the MacGyver framework. Furthermore, inspired by existing theories of creativity, we extend the MacGyver problem formalism to incorporate subjective observers of problem solving tasks. By doing this, we explicitly model solutions to creative problem solving tasks as subjective evaluations based on the varying domain knowledge of observing agents. As an application of our extended formalism, we then illustrate how previous work on goal-driven conceptual blending represents a powerful form of human creativity whose creative solutions can be more formally described through our classes of novel solutions.

We propose a categorization of solution-centric evaluation metrics for a class of domain-independent AI challenge tasks known as MacGyver problems. Our definitions formally describe different classes of novel solutions for general creative problem solving tasks described in the MacGyver framework. Furthermore, inspired by existing theories of creativity, we extend the MacGyver problem formalism to incorporate subjective observers of problem solving tasks. By doing this, we explicitly model solutions to creative problem solving tasks as subjective evaluations based on the varying domain knowledge of observing agents. As an application of our extended formalism, we then illustrate how previous work on goal-driven conceptual blending represents a powerful form of human creativity whose creative solutions can be more formally described through our classes of novel solutions. Additionally, we conclude by highlighting strong connections between observer-oriented creative problem solving as described here and personalized procedural content generation in games.