If you use an AI image or text generator to make a work of "art," does it belong to you? That's a huge question hanging over the heads of anyone tempted to use AI tools for commercial products. Crucially, simply plugging prompts into an AI image generator or text generator does NOT meet this burden. Because the author (or artist, or other relevant creative term) of a work is defined as "the person who translates an idea into a fixed, tangible expression," an AI system cannot meet this burden, even though it's using input from a human to generate its output. Commenting on established case law, the report says that "…the Supreme Court has made clear that originality is required, not just time and effort."

ARTICLE TEMPLATE Beyond Detection: Designing AI-Resilient Assessments with Automated Feedback Tool to Foster Critical Thinking and Originality Muhammad Sajjad Akbar a a University of Sydney, Australia; ARTICLE HISTORY Compiled April 1, 2025 ABSTRACT The growing prevalence of generative AI tools such as ChatGPT has raised urgent concerns about their impact on student learning, particularly their potential to erode critical thinking and creativity in academic contexts. As students increasingly use these tools to complete assessments, foundational cognitive skills are at risk of being bypassed, challenging the integrity of higher education and the authenticity of student work. Current AI-generated text detection tools are fundamentally inadequate in addressing this challenge. They produce unreliable, unverifiable outputs and are highly susceptible to false positives and false negatives, especially when students apply obfuscation techniques such as paraphrasing, translation, or structural rewording. These tools rely on shallow statistical features rather than contextual or semantic understanding, making them unsuitable as definitive indicators of AI misuse. In response, this research proposes an AI-resilient, assessment-based solution that shifts focus from reactive detection to proactive assessment design. The solution is delivered through a web-based Python tool that integrates Bloom's Taxonomy with advanced natural language processing techniques including GPT-3.5 Turbo, BERT-based semantic similarity, and TF-IDF metrics to evaluate the AI-solvability of assignment tasks. By analyzing both surface-level and semantic features, the tool helps educators assess whether a task targets lower-order thinking (e.g., recall, summarization), which is more easily completed by AI, or higher-order skills (e.g., analysis, evaluation, creation), which are more resistant to AI automation. This framework empowers educators to intentionally design cognitively demanding AI-resistant assessments that promote originality, critical thinking, and fairness. By addressing the design of root issue assessment rather than relying on flawed detection tools, this research contributes a sustainable and pedagogically sound strategy to uphold academic standards and foster authentic learning in the era of AI. KEYWORDS Generative AI; ChatGPT; AI-resilient; Bloom's Taxonomy; Automated Assessments; AI-solvability;Automated Feedback; appendices 1. Introduction Integrating AI-technology with innovative thinking skills in higher education (HE) environment has grown more challenging due to rapid digital innovation and ubiquitous data availability. In applied education, innovative thinking is essential. It is charac-CONTACT Muhammad Sajjad Akbar. It entails thinking creatively to come up with original solutions to issues, enhance workflows, or open up new possibilities.

A text generation model is a machine learning model that uses neural networks, especially transformers architecture to generate contextually relevant text based on linguistic patterns learned from extensive corpora. The models are trained on a huge amount of textual data so that they can model and learn complex concepts of any language like its grammar, vocabulary, phrases, and styles. Text generation models can increase the productivity of humans in their current business processes. These models are already automating the process of content creation across industries for the generation of reports, summaries, and emails among others. These models are also allowing for a greater level of personalization in communications between businesses and their customers.

With the advancement in capabilities of Large Language Models (LLMs), one major step in the responsible and safe use of such LLMs is to be able to detect text generated by these models. While supervised AI-generated text detectors perform well on text generated by older LLMs, with the frequent release of new LLMs, building supervised detectors for identifying text from such new models would require new labeled training data, which is infeasible in practice. In this work, we tackle this problem and propose a domain generalization framework for the detection of AI-generated text from unseen target generators. Our proposed framework, EAGLE, leverages the labeled data that is available so far from older language models and learns features invariant across these generators, in order to detect text generated by an unknown target generator. EAGLE learns such domain-invariant features by combining the representational power of self-supervised contrastive learning with domain adversarial training. Through our experiments we demonstrate how EAGLE effectively achieves impressive performance in detecting text generated by unseen target generators, including recent state-of-the-art ones such as GPT-4 and Claude, reaching detection scores of within 4.7% of a fully supervised detector.

This article is from Big Technology, a newsletter by Alex Kantrowitz. This week, Microsoft and Google introduced generative A.I. tools that make attending meetings, writing emails, scheduling travel, and catching up on projects vastly easier. The products channel the wonder of buzzy A.I. products like ChatGPT, DALL-E, Midjourney, and Bard into clear, applicable uses. And these obvious uses just happen to be in the workplace. About a year into the generative A.I. phenomenon, it's becoming evident that the technology is most useful in enterprise first, with broader consumer adoption perhaps to follow.

OpenAI's ChatGPT seems ubiquitous, but open source versions of instruction-tuned text generators are gaining the upper hand. In just 6 months, at least 15 serious alternatives have emerged, all of which have at least one important advantage over ChatGPT: they are a lot more transparent. Insight into training data and algorithms is key for responsible use of generative AI, a team of linguists and language technology researchers at Radboud University claim. The researchers have mapped this rapidly evolving landscape in a paper and a live-updated website. This shows there are many working alternative "open source" text generators, but also that openness comes in degrees and that many models inherit legal restrictions.

During the 2016 U.S. presidential race, a Russian "troll-farm" calling itself the Internet Research Agency sought to harm Hillary Clinton's election chances and help Donald Trump reach the White House by using Twitter to spread false news stories and other disinformation, according to a 2020 report from the Senate Intelligence Committee. Most of that content apparently was produced by human beings, a supposition supported by the fact that activity dropped off on Russian holidays. Soon, though, if not already, such propaganda will be produced automatically by artificial intelligence (AI) systems such as ChatGPT, a chatbot capable of creating human-sounding text. "Imagine a scenario where you have ChatGPT generating these tweets. The number of fake accounts you could manage for the same price would be much larger," says V.S. Subrahmanian, a professor of computer science at Northwestern University, whose research focuses on the intersection of AI and security problems.

In February, 2019, OpenAI, a little-known artificial-intelligence company, announced that its large-language-model text generator, GPT-2, would not be released to the public "due to our concerns about malicious applications of the technology." Among the dangers, the company stated, was a potential for misleading news articles, online impersonation, and automating the production of abusive or faked social-media content and of spam and phishing content. As a consequence, Open AI proposed that "governments should consider expanding or commencing initiatives to more systematically monitor the societal impact and diffusion of AI technologies, and to measure the progression in the capabilities of such systems." This week, four years after that warning, members of the Senate Judiciary Subcommittee on Privacy, Technology, and the Law met to discuss "Oversight of A.I.: Rules for Artificial Intelligence." As has been the case with other tech hearings on the Hill, this one came after a new technology with the capacity to fundamentally alter our social and political lives was already in circulation. Like many Americans, the lawmakers became concerned about the pitfalls of large-language-model artificial intelligence in March, when OpenAI released GPT-4, the latest and most polished iteration of its text generator.

In second language vocabulary learning, existing works have primarily focused on either the learning interface or scheduling personalized retrieval practices to maximize memory retention. However, the learning content, i.e., the information presented on flashcards, has mostly remained constant. Keyword mnemonic is a notable learning strategy that relates new vocabulary to existing knowledge by building an acoustic and imagery link using a keyword that sounds alike. Beyond that, producing verbal and visual cues associated with the keyword to facilitate building these links requires a manual process and is not scalable. In this paper, we explore an opportunity to use large language models to automatically generate verbal and visual cues for keyword mnemonics. Our approach, an end-to-end pipeline for auto-generating verbal and visual cues, can automatically generate highly memorable cues. We investigate the effectiveness of our approach via a human participant experiment by comparing it with manually generated cues.

AI text generators, also known as language models, are algorithms that use artificial intelligence to generate human-like text based on a given prompt or seed text. These models are trained on vast amounts of text data, learning patterns, and relationships within the data to produce coherent and meaningful responses. AI or ML text generators can be used in a wide range of applications, including chatbots, customer service, content creation, and even creative writing. The Global AI Text Generator Market was valued at USD 360 million in 2022 and is expected to grow at a CAGR of 18% during the forecast period of 2023-2032 to reach USD 1,808 million. AI/ML generators can be used to produce responses to user queries in a conversational and natural manner, making them useful in developing chatbots for customer service or online support.