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The capabilities of large language models (LLMs) have advanced to the point where entire textbooks can be queried using retrieval-augmented generation (RAG), enabling AI to integrate external, up-to-date information into its responses. This study evaluates the ability of two OpenAI models, GPT-3.5 Turbo and GPT-4 Turbo, to create and answer exam questions based on an undergraduate textbook. 14 exams were created with four true-false, four multiple-choice, and two short-answer questions derived from an open-source Pacific Studies textbook. Model performance was evaluated with and without access to the source material using text-similarity metrics such as ROUGE-1, cosine similarity, and word embeddings. Fifty-six exam scores were analyzed, revealing that RAG-assisted models significantly outperformed those relying solely on pre-trained knowledge. GPT-4 Turbo also consistently outperformed GPT-3.5 Turbo in accuracy and coherence, especially in short-answer responses. These findings demonstrate the potential of LLMs in automating exam generation while maintaining assessment quality. However, they also underscore the need for policy frameworks that promote fairness, transparency, and accessibility. Given regulatory considerations outlined in the European Union AI Act and the NIST AI Risk Management Framework, institutions using AI in education must establish governance protocols, bias mitigation strategies, and human oversight measures. The results of this study contribute to ongoing discussions on responsibly integrating AI in education, advocating for institutional policies that support AI-assisted assessment while preserving academic integrity. The empirical results suggest not only performance benefits but also actionable governance mechanisms, such as verifiable retrieval pipelines and oversight protocols, that can guide institutional policies.
This study investigates the incorporation of advanced heating, ventilation, and air conditioning (HVAC) systems with reinforcement learning (RL) control to enhance energy efficiency in low-energy buildings amid the extreme seasonal temperatures of Tehran. We conducted comprehensive simulation assessments using the EnergyPlus and HoneybeeGym platforms to evaluate two distinct reinforcement learning models: traditional Q-learning (Model A) and deep reinforcement learning (DRL) with neural networks (Model B). Model B consisted of a deep convolutional network architecture with 256 neurons in each hidden layer, employing rectified linear units as activation functions and the Adam optimizer at a learning rate of 0.001. The results demonstrated that the RL-managed systems resulted in a statistically significant reduction in energy-use intensity of 25 percent (p < 0.001), decreasing from 250 to 200 kWh/m² annually in comparison to the baseline scenario. The thermal comfort showed notable improvements, with the expected mean vote adjusting to 0.25, which falls within the ASHRAE Standard 55 comfort range, and the percentage of anticipated dissatisfaction reduced to 10%. Model B (DRL) demonstrated a 50 percent improvement in prediction accuracy over Model A, with a mean absolute error of 0.579366 compared to 1.140008 and a root mean square error of 0.689770 versus 1.408069. This indicates enhanced adaptability to consistent daily trends and irregular periodicities, such as weather patterns. The proposed reinforcement learning method achieved energy savings of 10–15 percent compared to both rule-based and model predictive control and approximately 10 percent improvement over rule-based control, while employing fewer building features than existing state-of-the-art control systems.
After acquiring sufficient vocabulary in a foreign language, learners start understanding parts of conversations in that language. Speaking, in contrast, is a harder task. Forming grammatical sentences requires choosing the right tenses and following syntax rules. Every beginner EFL speaker makes grammar errors – and the type of grammar errors can reveal hints about their native language. For instance, Russian speakers tend to omit the determiner “the” because Russian doesn’t use such modifying words. One linguistic phenomenon that is actually easier in English than in many other languages is grammatical gender. English doesn’t assign gender to inanimate nouns such as “table” or “cup.” A few years ago, the differences in grammatical gender between languages helped reveal societal gender bias in automatic translation: translation systems that were shown gender-neutral statements in Turkish about doctors and nurses assumed that the doctor was male while the nurse was female.
At what time does the afternoon start, at 1 p.m. or 3 p.m.? Language understanding requires the ability to correctly match statements to their real-world meaning. This mapping process is a function of the context, which includes various factors such as location and time as well as the speaker’s and listeners’ backgrounds. For example, an utterance like, “It is hot today,” would mean different things were it expressed in Death Valley versus Alaska. Based on our background and experiences, people have different interpretations for time expressions, color descriptions, geographic expressions, qualities, relative expressions, and more. This ability to map language to real-world meaning is also required from the language technology tools we use. For example, translating a recipe that contains instructions to “preheat the oven to 180 degrees” requires a translation system to understand the implicit scale (e.g. Celsius versus Fahrenheit) based on the source language and the user’s location. To date, no automatic translation systems can do this, and there is little “grounding” in any widely used language technology tool.
In order to be effective mathematics educators, teachers need more than content knowledge: they need to be able to make mathematics comprehensible and accessible to their students. Teaching Key Concepts in the Australian Mathematics Curriculum Years 7 to 10 ensures that pre-service and practising teachers in Australia have the tools and resources required to teach lower secondary mathematics.
By simplifying the underlying concepts of mathematics, this book equips teachers to design and deliver mathematics lessons at the lower secondary level. The text provides a variety of practical activities and teaching ideas that translate the latest version of the Australian Curriculum into classroom practice. It covers the challenges of middle year mathematics, including the current decline in student numeracy, as well as complex theories which teachers can struggle to explain clearly. Topics include number, algebra, measurement, space, statistics and probability. Whether educators have recently studied more complicated mathematics or are teaching out of field, they are supported to recall ideas and concepts that they may have forgotten – or that may not have been made explicit in their own education.
Authored by experienced classroom educators and academics, this book is a vital resource for pre-service and practising Years 7 to 10 mathematics teachers, regardless of their backgrounds and experiences.
In order to be effective mathematics educators, teachers need more than content knowledge: they need to be able to make mathematics comprehensible and accessible to their students. Teaching Key Concepts in the Australian Mathematics Curriculum Years 7 to 10 ensures that pre-service and practising teachers in Australia have the tools and resources required to teach lower secondary mathematics.
By simplifying the underlying concepts of mathematics, this book equips teachers to design and deliver mathematics lessons at the lower secondary level. The text provides a variety of practical activities and teaching ideas that translate the latest version of the Australian Curriculum into classroom practice. It covers the challenges of middle year mathematics, including the current decline in student numeracy, as well as complex theories which teachers can struggle to explain clearly. Topics include number, algebra, measurement, space, statistics and probability. Whether educators have recently studied more complicated mathematics or are teaching out of field, they are supported to recall ideas and concepts that they may have forgotten – or that may not have been made explicit in their own education.
Authored by experienced classroom educators and academics, this book is a vital resource for pre-service and practising Years 7 to 10 mathematics teachers, regardless of their backgrounds and experiences.
Non-compositional phrases such as “by and large” are phrases whose meaning cannot be unlocked by simply translating the combination of words they constitute. In particular, figurative expressions – such as idioms, similes and metaphors – are ubiquitous in English. Among other reasons, figurative expressions are acquired late in the language learning journey because they often capture cultural conventions and social norms associated with the people speaking the language. Figurative expressions are especially prevalent in creative writing, acting as the spice that adds flavor to the writing. Artificial intelligence (AI) writing assistants such as ChatGPT are now capable of editing raw drafts into well-written pieces, to the advantage of native and non-native speakers alike. These AI tools, which have gained their writing skills from exposure to vast amounts of online text, are extremely adept at generating text similar to the texts they have been exposed to. Unfortunately, they have demonstrated shortcomings in creative writing that requires deviating from the norm.
In order to be effective mathematics educators, teachers need more than content knowledge: they need to be able to make mathematics comprehensible and accessible to their students. Teaching Key Concepts in the Australian Mathematics Curriculum Years 7 to 10 ensures that pre-service and practising teachers in Australia have the tools and resources required to teach lower secondary mathematics.
By simplifying the underlying concepts of mathematics, this book equips teachers to design and deliver mathematics lessons at the lower secondary level. The text provides a variety of practical activities and teaching ideas that translate the latest version of the Australian Curriculum into classroom practice. It covers the challenges of middle year mathematics, including the current decline in student numeracy, as well as complex theories which teachers can struggle to explain clearly. Topics include number, algebra, measurement, space, statistics and probability. Whether educators have recently studied more complicated mathematics or are teaching out of field, they are supported to recall ideas and concepts that they may have forgotten – or that may not have been made explicit in their own education.
Authored by experienced classroom educators and academics, this book is a vital resource for pre-service and practising Years 7 to 10 mathematics teachers, regardless of their backgrounds and experiences.
Even after achieving a high level of English proficiency, our accents – along with involuntary code-switching, pronunciation of English words as they are pronounced in our native tongue, and more – may still give us away as EFLs. Accent is the most immediately noticeable feature of EFL speakers. After moving to North America, I was faced with a conflict: Should I preserve my foreign accent and embrace it as part of my identity or try to pass as an American? While the perception that all accents are valid is true, it is also – to some extent – naïve. It not only ignores the desire to assimilate into American culture but also minimizes the impact of implicit biases, which can go as far as labeling people with foreign accents as less competent. Another practical reason to develop a North American accent is to adjust to personal assistants such as Siri and Alexa that often fail to understand foreign accents. At the same time as the world is becoming more progressive and inclusive, language technology sometimes inadvertently pushes us a step back.
Language learning is often regarded as beneficial for developing a higher level of empathy and cultural appreciation. When we connect with people from a different linguistic background than ours, we can catch a glimpse of the rich cultural and linguistic mosaic that makes up our world – and incorporate these insights into our perspective of humanity. We also recognize that there are certain compromises that EFL speakers face when they make English their dominant day-to-day means of communication. One is the loss of proficiency in their native language, which can include forgetting words and code-switching to English; the second is a change in identity as we adapt our sense of self to each language we speak. Examining these crises related to language and identity can help us map out a future for how we want to communicate – and for how language learning and language technologies can help us realize our vision.
Euphemisms, a particular type of idiom especially prevalent in American English, are vague or indirect expressions that often substitute harsh, embarrassing, or unpleasant terms. They are widely used to navigate sensitive topics like death and sex. “Passing away,” for example, has long been an accepted term to describe the act of dying. When euphemisms are in use for the length of time it takes to become lexicalized, they are often replaced with new ones, a phenomenon known as “the euphemism treadmill.” Correctly interpreting and using euphemisms can be difficult for EFL learners – and can lead to misuse since these expressions may rely on relevant cultural knowledge. That is unfortunate, given that euphemisms hold sensitive meanings. Artificial intelligence (AI) writing assistants can now go beyond grammar correction to suggesting edits for more inclusive language, such as replacing “whitelist” with “allow-list” and “landlord” with “property owner.” Such suggestions can help inform EFLs and users from diverse cultures – who carry a different cultural baggage – of unintended bias in their writing. At the same time, these assistants also run the risk of erasing individual and cultural differences.
Apart from the words we speak or write, nonverbal communication – such as tone of voice, facial expressions, eye contact, and gestures – also differs across cultures. For example, travel guides for Italy like to warn against using the 🤌 hand gesture commonly signaling “wait” in many countries, because Italians interpret this gesture as, “What the hell are you saying?” Tech companies are now dipping their toes into analyzing users’ behavior as expressed in nonverbal communication. For example, Zoom is providing business customers with AI tools that can determine users’ emotions during video calls based on facial expressions and tone of voice. Unless companies carefully consider cultural differences, the ramifications could be more algorithmic bias and discrimination.