Welcome to this IELTS Practice Reading Test on Artificial Intelligence.
This practice test is designed to help you prepare for the IELTS Reading section by providing realistic reading passages and questions similar to those you might encounter on test day.
This includes 7 summary completion questions that require you to demonstrate your understanding of the main ideas and details in the passages.
Time recommendation: Allocate approximately 20 minutes to complete each version of the test. This will help you practice working under time constraints similar to the actual exam.
Remember to read the instructions carefully and answer all questions.
Good luck!
Artificial Intelligence
A Artificial intelligence represents one of the most significant technological revolutions in human history, promising to fundamentally transform numerous aspects of society, from healthcare and transportation to economic systems and governance structures. Contemporary AI research encompasses a diverse range of approaches and methodologies, though machine learning—particularly deep learning—has emerged as the dominant paradigm. These systems utilize multi-layered neural networks inspired by the human brain’s architecture to identify patterns in vast datasets and make increasingly sophisticated predictions. Unlike traditional software, which follows explicit programming instructions, modern AI systems derive their capabilities from exposure to enormous quantities of training data, developing complex internal representations that often elude complete human understanding.
B The current AI landscape is characterized by remarkable achievements in specific domains, with systems demonstrating superhuman performance in tasks ranging from image recognition to strategic gameplay. In 2016, DeepMind’s AlphaGo defeated world champion Lee Sedol at Go, a game of profound complexity that many experts had predicted would remain beyond computational mastery for decades. Computer vision systems now exceed human accuracy in identifying visual abnormalities in medical imaging, while natural language processing models can generate coherent and contextually appropriate text across diverse topics and styles. These accomplishments represent significant milestones in computational capability, yet they exist within the framework of what researchers classify as “narrow” or “weak” AI—systems designed for specific applications rather than exhibiting the generalized intelligence characteristic of human cognition.
C Despite these impressive advancements, contemporary AI systems encounter substantial limitations that differentiate them from human intelligence. Most fundamentally, they lack genuine understanding of the information they process. A language model might generate persuasive text about quantum physics without comprehending the underlying concepts or recognizing when its statements contradict established scientific principles. Similarly, an image recognition system that performs flawlessly under normal conditions may catastrophically fail when presented with slightly modified inputs—demonstrating a brittleness incompatible with true intelligence. These systems also require exponentially more data than humans to learn effectively; while a child can recognize a new animal after seeing just one or two examples, an AI typically needs thousands or millions of labeled instances to achieve comparable performance.
D The relationship between computational power and AI capability represents another critical dimension of current research. Historical trends suggest that AI performance correlates strongly with computational resources, with larger models trained on more extensive datasets consistently outperforming their smaller counterparts. This observation has prompted some researchers to propose that artificial general intelligence—systems with human-level performance across diverse domains—might emerge naturally from sufficiently large networks trained on sufficiently comprehensive data. Others contend that qualitative breakthroughs in architectural design or learning methodologies will be necessary to bridge the gap between narrow and general intelligence. This debate reflects fundamental disagreements about the nature of intelligence itself and whether our current trajectory will eventually lead to systems with human-like cognitive capabilities.
E The societal implications of advanced AI systems extend far beyond technical considerations, encompassing profound economic, ethical, and philosophical questions. Employment disruption represents perhaps the most immediate concern, as increasingly capable AI systems assume tasks traditionally performed by human workers. Unlike previous waves of automation, which primarily affected routine physical labor, contemporary AI threatens to displace knowledge workers across numerous sectors, from legal research to financial analysis. While technological advancement has historically generated more employment opportunities than it eliminated, the unprecedented scope and pace of AI-driven automation may challenge this pattern, necessitating novel approaches to education, labor policy, and social welfare.
F Ethical considerations surrounding AI development have attracted increasing attention from researchers, policymakers, and the general public. Issues of algorithmic bias and fairness have emerged as particularly urgent concerns, as systems trained on historical data often reproduce and amplify existing societal prejudices. For example, facial recognition technologies have demonstrated significantly higher error rates for women and individuals with darker skin tones, while hiring algorithms have shown systematic biases against female candidates. These disparities reflect both the composition of training datasets and the implicit values embedded in algorithmic design choices. Addressing such biases requires diverse development teams, representative data, and explicit consideration of fairness metrics throughout the design process.
G Questions of transparency and accountability present additional challenges for AI governance. Many contemporary systems, particularly deep learning models, function as “black boxes” whose decision-making processes resist straightforward interpretation. This opacity becomes especially problematic in high-stakes domains such as healthcare, criminal justice, and financial services, where affected individuals have legitimate interests in understanding the factors influencing algorithmic determinations. Various technical approaches to explainable AI have emerged in recent years, though fundamental tensions exist between performance optimization and interpretability. Regulatory frameworks increasingly emphasize transparency requirements, reflecting the growing consensus that meaningful human oversight requires some degree of algorithmic explicability.
H Looking toward the future, the development of artificial general intelligence (AGI)—systems with human-level performance across diverse cognitive domains—represents both the field’s ultimate aspiration and its most profound existential question. While contemporary systems remain firmly within the narrow AI paradigm, progress toward more general capabilities continues at an accelerating pace. The emergence of AGI would constitute an unprecedented milestone in human history, potentially offering solutions to humanity’s most intractable problems while simultaneously presenting novel risks. Ensuring that advanced AI systems remain aligned with human values and interests represents one of the central challenges of our time, requiring collaborative engagement across technical, ethical, and policy domains. As we navigate this complex landscape, maintaining a balanced perspective—neither dismissing legitimate concerns nor succumbing to apocalyptic thinking—will be essential for realizing AI’s transformative potential while mitigating its associated risks.
Summary Completion Questions for Academic Reading
Complete the summary below.
Choose NO MORE THAN THREE WORDS AND/OR A NUMBER from the passage for each answer.
The Current State and Future Implications of Artificial Intelligence
Modern artificial intelligence research is primarily focused on _________(1), particularly deep learning approaches that utilize neural networks modeled after the human brain. Unlike conventional computer programs that follow explicit instructions, these systems develop their capabilities by analyzing _________(2) and creating complex internal patterns.
Despite impressive achievements in specific areas like image recognition and strategic games, today’s AI systems are classified as ________(3) AI because they lack generalized intelligence. A fundamental limitation is that these systems don’t truly _________(4) the information they process, which can lead to failures when encountering unfamiliar scenarios.
There is ongoing debate about whether achieving artificial general intelligence will result naturally from increasing _________(5) or will require fundamental breakthroughs in system design. The societal impact of advanced AI systems raises concerns about _________(6) disruption, as these technologies now threaten positions in knowledge-based fields rather than just routine manual labor.
Ethical challenges include algorithmic bias and fairness issues, as AI systems often _________(7) existing societal prejudices found in their training data. Addressing these concerns requires diverse development teams and explicit fairness considerations throughout the design process.
Have you completed the test?
Write your answers in the comments section below.
We’ll provide feedback on your responses and help you understand any questions you found challenging.
For more IELTS Reading Practices, you can visit Space Satellites Topic.
Good luck with your IELTS preparation!
Leave a Reply