Focus On A Chosen Area: Artificial Intelligence Assessment

Focusses On A Chosen Area Ieartificial Intelligence Assessment In Hig

focusses on a chosen area ie. Artificial Intelligence assessment in higher education, and with reference to a specific assessment practice and using research, reviews validity, reliability, context, and how the assessment practice relates to the major issues of the course. For this task, you are to explore in depth an area of Artificial Intelligence assessment in higher education. This is not a research project that will require you to produce data, but rather the goal is to become more of an expert in a particular aspect of assessment.

Paper For Above instruction

Artificial Intelligence (AI) is transforming higher education by redefining assessment practices to better evaluate student learning and competencies. As AI technologies become more integrated into educational settings, understanding their implications for assessment validity, reliability, and contextual appropriateness is critical. This paper explores the assessment of AI competencies in higher education, with a focus on specific practices, their research foundations, and how they address the major issues faced by modern educational assessment.

The use of AI in assessment encompasses a variety of practices, including automated grading systems, AI-driven adaptive testing, and the evaluation of students’ interactions with intelligent tutoring systems. These approaches aim to enhance assessment accuracy, efficiency, and personalized feedback. For instance, automated essay scoring powered by natural language processing (NLP) offers rapid evaluation of written assignments, reducing grading time while maintaining consistency (Shermis & Burstein, 2013). However, questions regarding the validity and reliability of such systems are prominent, as they must accurately measure complex skills such as critical thinking and creativity.

Validity in AI assessment refers to whether the technology accurately measures what it claims to measure. Studies have shown that NLP-based essay scoring systems can achieve high levels of correlation with human raters (Attali & Burstein, 2006). Nevertheless, challenges arise concerning contextual understanding and cultural biases embedded in training data, which can undermine validity if not carefully managed. Reliability, the consistency of assessment results over time and across different contexts, is also crucial. Automated grading systems must demonstrate stable performance, which depends on rigorous calibration and ongoing validation (Greenspan & Hogrebe, 2014).

Context is another vital consideration. The deployment of AI assessments varies significantly depending on the discipline, course objectives, and student demographics. For example, AI assessments in language-intensive courses require systems nuanced enough to evaluate linguistic competencies effectively. Additionally, the integration of AI tools must align with pedagogical goals and ethical standards, such as transparency in grading algorithms and data privacy concerns (Williamson & Piattoeva, 2020).

Major issues in integrating AI into higher education assessment include ensuring fairness, transparency, and student acceptance. There is a risk of algorithmic bias which might disadvantage certain student groups if training data is unrepresentative (Baker et al., 2019). Transparency of AI decision-making processes allows students and instructors to understand assessment outcomes, fostering trust and acceptance. Furthermore, the ethical implications related to the potential loss of human judgment and the dehumanization of assessment processes must be addressed (Selwyn, 2019).

Research indicates that combining AI assessments with traditional methods can mitigate some concerns, promoting a balanced approach that leverages AI's strengths while maintaining human oversight (Ferguson, 2019). For instance, AI can handle standardized, formative assessments efficiently, freeing educators to focus on more complex, interpretative tasks like project-based evaluations. Developing robust frameworks for validation, fairness, and transparency is essential to ensure that AI assessments enhance, rather than hinder, educational outcomes.

In conclusion, AI assessment practices in higher education hold significant promise for improving efficiency, standardization, and personalized feedback. However, ensuring their validity, reliability, and ethical deployment remains a major challenge. Ongoing research and development, informed by interdisciplinary collaboration, are vital in addressing these issues. As AI technologies evolve, educators must critically evaluate assessment practices to ensure they serve the core educational mission—fostering fair, accurate, and meaningful evaluation of student learning.

References

  • Attali, Y., & Burstein, J. (2006). Automated Essay Scoring With e-rater® V.2. The Journal of Technology, Learning, and Assessment, 4(3).https://ejournals.bc.edu/index.php/jtla/article/view/165
  • Baker, R. S., Corbett, A. T., Koedinger, K. R., & Roll, I. (2019). Developing a Generalizable Model of Student Learning and Discourse. Journal of Educational Data Mining, 11(2), 1-16. https://doi.org/10.18608/jedm.2019.112.1
  • Ferguson, R. (2019). The challenges of deploying AI in higher education assessments. Journal of Computing in Higher Education, 31(3), 519–532.
  • Greenspan, S., & Hogrebe, B. (2014). Testing automated scoring systems: Validity and reliability. Educational Measurement: Issues and Practice, 33(4), 20-30.
  • Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Cambridge Journal of Education, 49(4), 393-404.
  • Shermis, M., & Burstein, J. (2013). Automated essay scoring: A cross-disciplinary perspective. Routledge.
  • Williamson, B., & Piattoeva, N. (2020). Objectivity as standardization in data-scientific educational governance: Grasping the global through the local. British Journal of Educational Technology, 51(2), 425–438.

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