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Generative AI vs AI tools in higher education
Generative AI vs AI tools in higher education
Generative artificial intelligence is nothing new to society. According to TechTarget, AI has existed since the 1960s but could not create “convincingly authentic images, videos and audio of real people” until 2014. Generative AI has become increasingly popular, and 56% of college students surveyed by Best Colleges said they had used AI on assignments or...
·theprairienews.com·
Generative AI vs AI tools in higher education
Use AI, But Use it Wisely (Support + Policy)
Use AI, But Use it Wisely (Support + Policy)
Use AI, But Use it Wisely It’s Important to Understand What AI Tools like ChatGPT are: AI tools are Large Language Models (LLMs) that rely on input and are both adaptive and formulaic. The goal of these tools are designed to create “human-like” conversation or writing. LLMs, like ChatGPT ...
·docs.google.com·
Use AI, But Use it Wisely (Support + Policy)
A new guide for responsible AI use in higher ed
A new guide for responsible AI use in higher ed
Generative artificial intelligence holds “tremendous promise” in nearly every facet of higher education, but there need to be guardrails, policies and strong governance for the technology, according to a new report.
·insidehighered.com·
A new guide for responsible AI use in higher ed
The (AI) sky isn’t falling
The (AI) sky isn’t falling
Students using generative AI to write their essays is a problem, but it isn’t a crisis, writes Christopher Hallenbrook. We have the tools to tackle the issue of artificial intelligence
·timeshighereducation.com·
The (AI) sky isn’t falling
Electronic Brainstorming With a Chatbot Partner: A Good Idea Due to Increased Productivity and Idea Diversity
Electronic Brainstorming With a Chatbot Partner: A Good Idea Due to Increased Productivity and Idea Diversity
Brainstorming is a creative technique that fosters collaboration to enhance idea generation. The occurrence of evaluation apprehension, a fear of being evaluated negatively by others, however, can stymy brainstorming. How the advantages of collaboration can be leveraged while evaluation apprehension is prevented is an open scientific and practical problem. In this brief research report, it is proposed that chatbots could provide a solution. Chatbots can be designed to share ideas with their users, facilitating inspiration. Compared to human beings, chatbots are also perceived as possessing limited agency and evaluative capacity. This could reduce evaluation apprehension. Given that chatbots are often embedded in a textual chat interface, social cues (picture, name, and description) can reinforce the perceived chatbot identity, enhancing its alleged effects on evaluation apprehension and subsequently on brainstorming performance. These conjectures were tested in an online 2 × 2 between-subjects experiment (n = 120) where people were instructed to brainstorm with a partner that was framed as either a chatbot or human being (but followed the same automated script), with or without the presence of social cues. The results showed that brainstorming with a chatbot led participants to produce more ideas, with more diversity than brainstorming with an alleged human being. Social cues enhanced the effect on idea diversity, but only with the chatbot. No significant effects on evalua...
·frontiersin.org·
Electronic Brainstorming With a Chatbot Partner: A Good Idea Due to Increased Productivity and Idea Diversity
GPT detectors are biased against non-native English writers
GPT detectors are biased against non-native English writers
The rapid adoption of generative language models has brought about substantial advancements in digital communication, while simultaneously raising concerns regarding the potential misuse of AI-generated content. Although numerous detection methods have been proposed to differentiate between AI and human-generated content, the fairness and robustness of these detectors remain underexplored. In this study, we evaluate the performance of several widely-used GPT detectors using writing samples from native and non-native English writers. Our findings reveal that these detectors consistently misclassify non-native English writing samples as AI-generated, whereas native writing samples are accurately identified. Furthermore, we demonstrate that simple prompting strategies can not only mitigate this bias but also effectively bypass GPT detectors, suggesting that GPT detectors may unintentionally penalize writers with constrained linguistic expressions. Our results call for a broader conversation about the ethical implications of deploying ChatGPT content detectors and caution against their use in evaluative or educational settings, particularly when they may inadvertently penalize or exclude non-native English speakers from the global discourse. The published version of this study can be accessed at: www.cell.com/patterns/fulltext/S2666-3899(23)00130-7
·arxiv.org·
GPT detectors are biased against non-native English writers