Reading Passage
Paragraph A
In 2010, the Centre for Digital Education Research (CDER), located at 14 Knowledge Way, Manchester, United Kingdom, began examining the potential of artificial intelligence (AI) to support university assessment systems. At the time, higher education institutions processed an estimated 1.6 billion examination scripts annually worldwide. Early investigations focused on automated grading software for multiple-choice and short-answer assessments. Between 2011 and 2013, pilot programmes at five British universities reported grading time reductions of up to 34%, prompting wider academic interest in AI-supported evaluation methods.
Paragraph B
Large-scale trials commenced in 2014, involving 27 universities across England and Scotland. AI systems were deployed to assess coursework in subjects such as economics, psychology, and computer science. According to CDER data, the average marking turnaround time fell from 21 days to 9 days. Implementation costs averaged 74,000 per institution, funded primarily through internal digital innovation budgets. Student surveys conducted in 2016 revealed that 68% of respondents felt feedback was delivered more quickly, though some expressed concerns about transparency.
Paragraph C
Despite efficiency gains, academic resistance emerged. A 2017 review found that 43% of lecturers doubted AIs ability to assess critical thinking and complex written arguments accurately. Ethical concerns were also raised regarding algorithmic bias and data privacy. Some institutions reported technical difficulties integrating AI tools with existing learning management systems. In response, CDER issued national guidelines in 2018 and organised training workshops in Birmingham and Leeds to support responsible implementation.
Paragraph D
Policy support increased after 2019, when the UK Department for Education approved a 210 million fund to promote digital assessment technologies. By 2021, 59% of public universities had adopted at least one AI-based assessment tool. National statistics indicated that administrative workload for academic staff declined by 18% between 2019 and 2022. These developments positioned the UK as a leading adopter of AI-assisted assessment within Europe.
Paragraph E
Beyond efficiency, financial and educational impacts were observed. A 2022 cost analysis conducted by the London School of Economics estimated annual savings of 320 million due to reduced staffing and processing costs. Student performance outcomes also improved modestly, with average course completion rates rising by 4.6% between 2020 and 2023. Additionally, complaints related to delayed feedback fell by 11%, according to data from the Office of the Independent Adjudicator.
Paragraph F
Future plans focus on expanding AI capabilities while strengthening oversight. CDER aims to introduce advanced natural language processing tools by 2030 to better evaluate analytical writing. As of February 2024, 46% of UK universities used advanced AI grading systems. Pilot studies scheduled for 2026 at institutions in York and Exeter will test hybrid models combining human and AI marking, with the long-term goal of improving fairness and academic integrity.
Questions 1-4: Matching Headings
Instructions: Choose the correct heading for each paragraph from the list below. Write the correct Roman numeral.
List of Headings
i. Financial and academic outcomes of AI use
ii. Early research into automated grading
iii. Academic concerns and ethical responses
iv. Widespread adoption supported by government funding
v. Operational changes during large-scale trials
vi. Long-term plans for hybrid assessment models
Question 1: Paragraph B
Question 2: Paragraph C
Question 3: Paragraph D
Question 4: Paragraph E
Questions 5-8: True/False/Not Given
Instructions: Write TRUE if the statement agrees with the information, FALSE if it contradicts, or NOT GIVEN if there is no information.
Question 5
AI grading systems were first tested in British universities before 2010.
Question 6
Marking turnaround time was reduced to less than two weeks during trials.
Question 7
All lecturers supported the use of AI for assessing written arguments.
Question 8
Student complaints about delayed feedback decreased after AI adoption.
Questions 9-12: Table Completion
Instructions: Complete the table below. Write NO MORE THAN TWO WORDS AND/OR A NUMBER for each answer.
| Category | Indicator | Result |
|---|---|---|
| Marking speed | Turnaround time | days |
| Implementation cost | Average per university | |
| Adoption rate (2021) | Public universities | percent |
| Administrative impact | Workload reduction | percent |
Questions 13-16: Sentence Completion
Instructions: Complete the sentences below. Write NO MORE THAN TWO WORDS AND/OR A NUMBER for each answer.
Question 13
Early pilot programmes reduced grading time by up to percent.
Question 14
Student surveys showed percent felt feedback was faster.
Question 15
National AI assessment guidelines were issued in .
Question 16
Annual financial savings were estimated at million.
Questions 17-19: Summary Completion
Instructions: Complete the summary below. Write NO MORE THAN TWO WORDS AND/OR A NUMBER for each answer.
Artificial intelligence was introduced into university assessment to reduce grading time and administrative workload. While efficiency improved, concerns about bias and ethical use were raised by academic staff. Government funding accelerated adoption, leading to financial savings and improved student rates. Future developments aim to combine AI tools with oversight to maintain educational standards.
Questions 9-13: Short Answer Questions
Instructions: Answer the questions below. Write NO MORE THAN THREE WORDS AND/OR A NUMBER for each answer.
Question 9
Where is the Centre for Digital Education Research located?
Question 10
How many universities participated in the 2014 trials?
Question 11
Which institution conducted the 2022 cost analysis?
Question 12
What percentage of universities used advanced AI systems in 2024?
Question 13
In which year are hybrid assessment pilot studies scheduled?