In recent years, authentic assessment has come to be?hailed as?something of an?educational cure-all, the solution to a?broad range of?complex problems, particularly in?programmes linked to?professions and industry.

It involves asking students to do?tasks that resemble ¡°real-world¡± scenarios, rather than abstract exams and essays. And it?has prompted educators to?reimagine assessments to make them more relevant and meaningful in?the eyes of?students, employers and others.

The trouble is, as colleagues and I?argue in a?, the enthusiasts often overlook a number of important questions about authentic assessment. What exactly is the ¡°real world¡±, for instance. How do we define ¡°authenticity¡±? And to?what, or to?whom, should these tasks be authentic: a?future workplace, society at large or the individual student?

A prime example of the unexamined rush to authentic assessment is the argument that it is the best way to prepare students for workplaces. The logic seems sound: by engaging with realistic tasks, students apply knowledge to situations they might encounter after graduation. In addition, authentic assessments are said to be more engaging, promoting richer, more complex learning experiences, in which students are motivated to invest themselves.

ÖйúAƬ

This personal investment, and the need to apply knowledge to realistic situations, is part of an argument that authentic assessments make cheating less likely. This idea is gaining traction because of its appeal to those worried about students using generative artificial intelligence (GenAI) to do their assessment work.

Others have said that authentic assessment is more inclusive because it allows students to demonstrate their knowledge and skills in diverse ways, which offer greater personal agency than standardised formats?do.

ÖйúAƬ

Yet it is risky to assume that authentic tasks will address such educational challenges. The evidence remains unclear. That probably has a lot to do with the fact that there are a huge range of assessment types that have been labelled as ¡°authentic¡±, including workplace-based assessments, simulation-based assessments, projects, problem-based learning, portfolios, report writing and even some scenario-based multiple-choice exam questions.

It also matters how things are done ¨C and doing authentic assessment well is difficult. It requires considerable expertise and attention to the particular aims of the assessment, the learners, the conditions in which the assessment is undertaken and the evidence required to demonstrate learning and achievement. An authentic assessment for first-year physics students would look very different from one designed for postgraduate nursing students or even for third- or fourth-year physics students.

Simply deciding to implement authentic assessment achieves nothing. The real work lies in designing targeted tasks and conditions, as well as negotiating trade-offs between authenticity and other concerns. An authentic assessment will also work best within a more ¡°authentic¡± educational approach, rather than expecting students to do challenging, authentic tasks from within a more traditional curriculum.

Another key question is whether an assessment needs to be authentic ¨C and, if so, to what extent, and why? It is often taken for granted that authenticity is desirable, but education involves scaffolding and support to develop critical thinking, deepen theoretical knowledge, and hone students¡¯ ability to make abstractions from applied contexts. There are difficult trade-offs to be made between authenticity and other aspirations, including assessment security, inclusion and even preparing students for life after graduation.

ÖйúAƬ

For instance, in preparing students for professional practice, the goal is not merely to have them replicate existing methods ¨C especially problematic ones ¨C but to equip them to think critically and drive positive change within workplaces. Therefore, simply mimicking employee tasks is both insufficient and ineffective.

Authentic assessment does not automatically reduce cheating, either. While it¡¯s tempting to think that GenAI is less capable of doing ¡°real-world¡± tasks, the reality depends on the specific task and context. Careful design of assessment security and promotion of academic integrity are still essential, and cheating and lapses in integrity are part of the authenticity of most contexts. And, of course, AI is becoming part of many workplace practices.

Then again, just allowing students to use AI is not automatically authentic: we need to know more about how the integration of technology within workplace practices is evolving. Moreover, we must consider what kinds of practices we want to promote through education and how they relate to the learning outcomes that are set for our students.

Making assessment more inclusive by allowing additional time or support, or giving students more choice in completing assignments, can also clash with authentic workplace contexts that are less flexible or accommodating. Consider the high-pressure environments of an emergency ward or a top-tier law firm. A challenge for ÖйúAƬ is to negotiate a balance between authenticity and promoting inclusive environments for learning.

ÖйúAƬ

If we¡¯re not careful, the authentic assessment label can be used to sidestep difficult conversations about the nuances of assessment and what constitutes appropriate evidence of learning and achievement in specific contexts. Labelling assessment as authentic doesn¡¯t remove the hard work of design or the challenging choices around the trade-offs between different goals and practical considerations.

Designing authentic assessments demands careful balancing with other educational goals. Rather than being adopted as a simple answer to complex issues, authenticity is more wisely used to encourage critical reflection about the challenges of preparing students for their future professional contexts.

ÖйúAƬ

is an associate professor (education-focused) at the Monash Education Academy, Monash University. ¡°¡± is published in Assessment &?Evaluation in?ÖйúAƬ.