This chapter addresses the citizenship challenges related to AI in education, particularly concerning students, teachers, and other educational stakeholders in the context of AI integration. We first explore how to foster AI awareness and education, along with various strategies to promote a socio-critical approach to AI training, aiming to identify relevant and ethical uses to prioritise. In the second part, we discuss critical thinking and computational thinking skills that can be mobilised within certain AI-supported educational activities, depending on the degree of creative and transformative engagement those activities require.

One day in September 2014, in a hamlet in the French Pyrenean foothills, Jean-Claude, a landscape gardener in his late 50s, was surprised to see his neighbour at the gate. He hadn't spoken to the 86-year-old in nearly 15 years after a dispute over a climbing rose that Jean-Claude had wanted to prune. The old man lived in total seclusion, tending to his garden in the djellaba he always wore, writing by night, heeding no one. Now, the long-bearded seeker looked troubled. "Would you do me a favour?" he asked Jean-Claude. "Could you buy me a revolver?" Then, after watching the hermit – who was deaf and nearly blind – totter erratically about his garden, he telephoned the man's children. Even they hadn't spoken to their father in close to 25 years. When they arrived in the village of Lasserre, the recluse repeated his request for a revolver, so he could shoot himself. There was barely room to move in his dilapidated house. The corridors were lined with shelves heaving with flasks of mouldering liquids.

Neural Architecture Search (NAS) benchmarks significantly improved the capability of developing and comparing NAS methods while at the same time drastically reduced the computational overhead by providing meta-information about thousands of trained neural networks. However, tabular benchmarks have several drawbacks that can hinder fair comparisons and provide unreliable results. These usually focus on providing a small pool of operations in heavily constrained search spaces -- usually cell-based neural networks with pre-defined outer-skeletons. In this work, we conducted an empirical analysis of the widely used NAS-Bench-101, NAS-Bench-201 and TransNAS-Bench-101 benchmarks in terms of their generability and how different operations influence the performance of the generated architectures. We found that only a subset of the operation pool is required to generate architectures close to the upper-bound of the performance range. Also, the performance distribution is negatively skewed, having a higher density of architectures in the upper-bound range. We consistently found convolution layers to have the highest impact on the architecture's performance, and that specific combination of operations favors top-scoring architectures. These findings shed insights on the correct evaluation and comparison of NAS methods using NAS benchmarks, showing that directly searching on NAS-Bench-201, ImageNet16-120 and TransNAS-Bench-101 produces more reliable results than searching only on CIFAR-10. Furthermore, with this work we provide suggestions for future benchmark evaluations and design. The code used to conduct the evaluations is available at https://github.com/VascoLopes/NAS-Benchmark-Evaluation.

Networks found with Neural Architecture Search (NAS) achieve state-of-the-art performance in a variety of tasks, out-performing human-designed networks. However, most NAS methods heavily rely on human-defined assumptions that constrain the search: architecture's outer-skeletons, number of layers, parameter heuristics and search spaces. Additionally, common search spaces consist of repeatable modules (cells) instead of fully exploring the architecture's search space by designing entire architectures (macro-search). Imposing such constraints requires deep human expertise and restricts the search to pre-defined settings. In this paper, we propose LCMNAS, a method that pushes NAS to less constrained search spaces by performing macro-search without relying on pre-defined heuristics or bounded search spaces. LCMNAS introduces three components for the NAS pipeline: i) a method that leverages information about well-known architectures to autonomously generate complex search spaces based on Weighted Directed Graphs with hidden properties, ii) an evolutionary search strategy that generates complete architectures from scratch, and iii) a mixed-performance estimation approach that combines information about architectures at initialization stage and lower fidelity estimates to infer their trainability and capacity to model complex functions. We present experiments in 13 different data sets showing that LCMNAS is capable of generating both cell and macro-based architectures with minimal GPU computation and state-of-the-art results. More, we conduct extensive studies on the importance of different NAS components in both cell and macro-based settings. Code for reproducibility is public at https://github.com/VascoLopes/LCMNAS.

Neural Architecture Search (NAS) methods have been successfully applied to image tasks with excellent results. However, NAS methods are often complex and tend to converge to local minima as soon as generated architectures seem to yield good results. This paper proposes GEA, a novel approach for guided NAS. GEA guides the evolution by exploring the search space by generating and evaluating several architectures in each generation at initialisation stage using a zero-proxy estimator, where only the highest-scoring architecture is trained and kept for the next generation. Subsequently, GEA continuously extracts knowledge about the search space without increased complexity by generating several off-springs from an existing architecture at each generation. More, GEA forces exploitation of the most performant architectures by descendant generation while simultaneously driving exploration through parent mutation and favouring younger architectures to the detriment of older ones. Experimental results demonstrate the effectiveness of the proposed method, and extensive ablation studies evaluate the importance of different parameters. Results show that GEA achieves state-of-the-art results on all data sets of NAS-Bench-101, NAS-Bench-201 and TransNAS-Bench-101 benchmarks.

In less than five years, artificial intelligence -- AI, as it's commonly known -- has gone from the stuff of science fiction to the forefront of the news, from scientific journals to the strategic plans of the world's biggest companies. It's a coming sea change, one that will disrupt entire parts of our lives by competing with what, until now, was seen as a fundamentally human characteristic: Our intelligence. At the same time, he acknowledges that AI -- at least for now -- is "still utterly unintelligent". Recognising a cat in a picture, a sentence dictated to a smartphone or even a tumour in MRI images is an impressive feat. But this prowess is still limited to very specific uses, Alexandre argues.

In less than five years, artificial intelligence -- AI, as it's commonly known -- has gone from the stuff of science fiction to the forefront of the news, from scientific journals to the strategic plans of the world's biggest companies. It's a coming sea change, one that will disrupt entire parts of our lives by competing with what, until now, was seen as a fundamentally human characteristic: our intelligence. At the same time, he acknowledges that AI -- at least for now -- is "still utterly unintelligent." Recognizing a cat in a picture, a sentence dictated to a smartphone or even a tumor in MRI images is an impressive feat. But the prowess is still limited to very specific uses, Alexandre argues. The real novelty that deep learning had promised to bring about, the futurologist explains, is that machines have now started to learn.