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Intellectual Property (IP) protection and management is the base of an entrepreneurship ecosystem. However, how to teach IP management is a relatively new topic in Asian universities. This paper reports a simulation method for teaching IP management and its impact on student learning motivation and behavior. This method uses a computer-aided software system and random-generated artefacts to simulate new product idea, execute patent trading and coordinate student teams to compete among each other while executing an IP management strategy. The project was implemented in two universities in Hong Kong in the past three years. It is found that the simulation system is quite effective in implementing action-based learning, increasing attention, and motivating students to practice more interactive learning and teamwork. It is also found that artefacts in the simulation system help competing teams to converge group thinking, induce the follow-up group actions promptly, and at the same time develop peer support and interactions for problem solving.

We propose an experimental method to study the possible emergence of sensemaking in artificial agents. This method involves analyzing the agent's behavior in a test bed environment that presents regularities in the possibilities of interaction afforded to the agent, while the agent has no presuppositions about the underlying functioning of the environment that explains such regularities. We propose a particular environment that permits such an experiment, called the Small Loop Problem. We argue that the agent's behavior demonstrates sensemaking if the agent learns to exploit regularities of interaction to fulfill its self-motivation as if it understood (at least partially) the underlying functioning of the environment. As a corollary, we argue that sensemaking and self-motivation come together. We propose a new method to generate self-motivation in an artificial agent called interactional motivation. An interactionally motivated agent seeks to perform interactions with predefined positive values and avoid interactions with predefined negative values. We applied the proposed sensemaking emergence demonstration method to an agent implemented previously, and produced example reports that suggest that this agent is capable of a rudimentary form of sensemaking.